diff --git "a/README.md" "b/README.md"
--- "a/README.md"
+++ "b/README.md"
@@ -1,1146 +1,1147 @@
----
-language:
-- de
-- en
-- es
-- fr
-- it
-- nl
-- pl
-- pt
-- ru
-- zh
-library_name: sentence-transformers
-tags:
-- sentence-transformers
-- sentence-similarity
-- feature-extraction
-- dataset_size:10K<n<100K
-- loss:MatryoshkaLoss
-- loss:CosineSimilarityLoss
-base_model: aari1995/gbert-large-2-cls-nlisim
-metrics:
-- pearson_cosine
-- spearman_cosine
-- pearson_manhattan
-- spearman_manhattan
-- pearson_euclidean
-- spearman_euclidean
-- pearson_dot
-- spearman_dot
-- pearson_max
-- spearman_max
-widget:
-- source_sentence: Ein Mann spricht.
-  sentences:
-  - Ein Mann spricht in ein Mikrofon.
-  - Der Mann spielt auf den Tastaturen.
-  - Zwei Mädchen gehen im Ozean spazieren.
-- source_sentence: Eine Flagge weht.
-  sentences:
-  - Die Flagge bewegte sich in der Luft.
-  - Ein Hund fährt auf einem Skateboard.
-  - Zwei Frauen sitzen in einem Cafe.
-- source_sentence: Ein Mann übt Boxen
-  sentences:
-  - Ein Affe praktiziert Kampfsportarten.
-  - Eine Person faltet ein Blatt Papier.
-  - Eine Frau geht mit ihrem Hund spazieren.
-- source_sentence: Das Tor ist gelb.
-  sentences:
-  - Das Tor ist blau.
-  - Die Frau hält die Hände des Mannes.
-  - NATO-Soldat bei afghanischem Angriff getötet
-- source_sentence: Zwei Frauen laufen.
-  sentences:
-  - Frauen laufen.
-  - Die Frau prüft die Augen des Mannes.
-  - Ein Mann ist auf einem Dach
-pipeline_tag: sentence-similarity
-model-index:
-- name: SentenceTransformer based on aari1995/gbert-large-2-cls-nlisim
-  results:
-  - task:
-      type: semantic-similarity
-      name: Semantic Similarity
-    dataset:
-      name: sts dev 1024
-      type: sts-dev-1024
-    metrics:
-    - type: pearson_cosine
-      value: 0.8417806877288009
-      name: Pearson Cosine
-    - type: spearman_cosine
-      value: 0.8452891310343582
-      name: Spearman Cosine
-    - type: pearson_manhattan
-      value: 0.8418749526406495
-      name: Pearson Manhattan
-    - type: spearman_manhattan
-      value: 0.8450348906331776
-      name: Spearman Manhattan
-    - type: pearson_euclidean
-      value: 0.8422615095001257
-      name: Pearson Euclidean
-    - type: spearman_euclidean
-      value: 0.8453390990427703
-      name: Spearman Euclidean
-    - type: pearson_dot
-      value: 0.8416625079549063
-      name: Pearson Dot
-    - type: spearman_dot
-      value: 0.8450616171323844
-      name: Spearman Dot
-    - type: pearson_max
-      value: 0.8422615095001257
-      name: Pearson Max
-    - type: spearman_max
-      value: 0.8453390990427703
-      name: Spearman Max
-  - task:
-      type: semantic-similarity
-      name: Semantic Similarity
-    dataset:
-      name: sts dev 768
-      type: sts-dev-768
-    metrics:
-    - type: pearson_cosine
-      value: 0.8418107096367227
-      name: Pearson Cosine
-    - type: spearman_cosine
-      value: 0.8453863409322975
-      name: Spearman Cosine
-    - type: pearson_manhattan
-      value: 0.8418527770289471
-      name: Pearson Manhattan
-    - type: spearman_manhattan
-      value: 0.8448328869253576
-      name: Spearman Manhattan
-    - type: pearson_euclidean
-      value: 0.8422791953749277
-      name: Pearson Euclidean
-    - type: spearman_euclidean
-      value: 0.8451547857394669
-      name: Spearman Euclidean
-    - type: pearson_dot
-      value: 0.8417682812591724
-      name: Pearson Dot
-    - type: spearman_dot
-      value: 0.8446927200809794
-      name: Spearman Dot
-    - type: pearson_max
-      value: 0.8422791953749277
-      name: Pearson Max
-    - type: spearman_max
-      value: 0.8453863409322975
-      name: Spearman Max
-  - task:
-      type: semantic-similarity
-      name: Semantic Similarity
-    dataset:
-      name: sts dev 512
-      type: sts-dev-512
-    metrics:
-    - type: pearson_cosine
-      value: 0.8394808864309438
-      name: Pearson Cosine
-    - type: spearman_cosine
-      value: 0.8437551103291275
-      name: Spearman Cosine
-    - type: pearson_manhattan
-      value: 0.8420246416513741
-      name: Pearson Manhattan
-    - type: spearman_manhattan
-      value: 0.8447335398769396
-      name: Spearman Manhattan
-    - type: pearson_euclidean
-      value: 0.8422722079216611
-      name: Pearson Euclidean
-    - type: spearman_euclidean
-      value: 0.8448909261141044
-      name: Spearman Euclidean
-    - type: pearson_dot
-      value: 0.8358204287638725
-      name: Pearson Dot
-    - type: spearman_dot
-      value: 0.8380004733308642
-      name: Spearman Dot
-    - type: pearson_max
-      value: 0.8422722079216611
-      name: Pearson Max
-    - type: spearman_max
-      value: 0.8448909261141044
-      name: Spearman Max
-  - task:
-      type: semantic-similarity
-      name: Semantic Similarity
-    dataset:
-      name: sts dev 256
-      type: sts-dev-256
-    metrics:
-    - type: pearson_cosine
-      value: 0.833879413726309
-      name: Pearson Cosine
-    - type: spearman_cosine
-      value: 0.8392439788855341
-      name: Spearman Cosine
-    - type: pearson_manhattan
-      value: 0.8379618268497928
-      name: Pearson Manhattan
-    - type: spearman_manhattan
-      value: 0.839860826315925
-      name: Spearman Manhattan
-    - type: pearson_euclidean
-      value: 0.838931461279174
-      name: Pearson Euclidean
-    - type: spearman_euclidean
-      value: 0.8404811150299943
-      name: Spearman Euclidean
-    - type: pearson_dot
-      value: 0.8230557648139373
-      name: Pearson Dot
-    - type: spearman_dot
-      value: 0.8242532718299653
-      name: Spearman Dot
-    - type: pearson_max
-      value: 0.838931461279174
-      name: Pearson Max
-    - type: spearman_max
-      value: 0.8404811150299943
-      name: Spearman Max
-  - task:
-      type: semantic-similarity
-      name: Semantic Similarity
-    dataset:
-      name: sts dev 128
-      type: sts-dev-128
-    metrics:
-    - type: pearson_cosine
-      value: 0.8253967606033702
-      name: Pearson Cosine
-    - type: spearman_cosine
-      value: 0.8335750690073012
-      name: Spearman Cosine
-    - type: pearson_manhattan
-      value: 0.8341588626988476
-      name: Pearson Manhattan
-    - type: spearman_manhattan
-      value: 0.8343994326050966
-      name: Spearman Manhattan
-    - type: pearson_euclidean
-      value: 0.8355263623880292
-      name: Pearson Euclidean
-    - type: spearman_euclidean
-      value: 0.8358857095028451
-      name: Spearman Euclidean
-    - type: pearson_dot
-      value: 0.8035163216908426
-      name: Pearson Dot
-    - type: spearman_dot
-      value: 0.8050271037746011
-      name: Spearman Dot
-    - type: pearson_max
-      value: 0.8355263623880292
-      name: Pearson Max
-    - type: spearman_max
-      value: 0.8358857095028451
-      name: Spearman Max
-  - task:
-      type: semantic-similarity
-      name: Semantic Similarity
-    dataset:
-      name: sts dev 64
-      type: sts-dev-64
-    metrics:
-    - type: pearson_cosine
-      value: 0.8150661334039712
-      name: Pearson Cosine
-    - type: spearman_cosine
-      value: 0.8265558538619309
-      name: Spearman Cosine
-    - type: pearson_manhattan
-      value: 0.8241988539394505
-      name: Pearson Manhattan
-    - type: spearman_manhattan
-      value: 0.8238763145175863
-      name: Spearman Manhattan
-    - type: pearson_euclidean
-      value: 0.8274925218859535
-      name: Pearson Euclidean
-    - type: spearman_euclidean
-      value: 0.8270778062044848
-      name: Spearman Euclidean
-    - type: pearson_dot
-      value: 0.7773847317840161
-      name: Pearson Dot
-    - type: spearman_dot
-      value: 0.7790338242936304
-      name: Spearman Dot
-    - type: pearson_max
-      value: 0.8274925218859535
-      name: Pearson Max
-    - type: spearman_max
-      value: 0.8270778062044848
-      name: Spearman Max
-  - task:
-      type: semantic-similarity
-      name: Semantic Similarity
-    dataset:
-      name: sts test 1024
-      type: sts-test-1024
-    metrics:
-    - type: pearson_cosine
-      value: 0.8130772714952826
-      name: Pearson Cosine
-    - type: spearman_cosine
-      value: 0.8188901246173036
-      name: Spearman Cosine
-    - type: pearson_manhattan
-      value: 0.8208715312691268
-      name: Pearson Manhattan
-    - type: spearman_manhattan
-      value: 0.8195095089412118
-      name: Spearman Manhattan
-    - type: pearson_euclidean
-      value: 0.820344720619671
-      name: Pearson Euclidean
-    - type: spearman_euclidean
-      value: 0.8189263018901494
-      name: Spearman Euclidean
-    - type: pearson_dot
-      value: 0.8127924456922464
-      name: Pearson Dot
-    - type: spearman_dot
-      value: 0.8185815083131535
-      name: Spearman Dot
-    - type: pearson_max
-      value: 0.8208715312691268
-      name: Pearson Max
-    - type: spearman_max
-      value: 0.8195095089412118
-      name: Spearman Max
-  - task:
-      type: semantic-similarity
-      name: Semantic Similarity
-    dataset:
-      name: sts test 768
-      type: sts-test-768
-    metrics:
-    - type: pearson_cosine
-      value: 0.8121757739236393
-      name: Pearson Cosine
-    - type: spearman_cosine
-      value: 0.8182913347635533
-      name: Spearman Cosine
-    - type: pearson_manhattan
-      value: 0.820604714791802
-      name: Pearson Manhattan
-    - type: spearman_manhattan
-      value: 0.8190481839997107
-      name: Spearman Manhattan
-    - type: pearson_euclidean
-      value: 0.8197462057663948
-      name: Pearson Euclidean
-    - type: spearman_euclidean
-      value: 0.8183157116237637
-      name: Spearman Euclidean
-    - type: pearson_dot
-      value: 0.8106698462984598
-      name: Pearson Dot
-    - type: spearman_dot
-      value: 0.8148932181769889
-      name: Spearman Dot
-    - type: pearson_max
-      value: 0.820604714791802
-      name: Pearson Max
-    - type: spearman_max
-      value: 0.8190481839997107
-      name: Spearman Max
-  - task:
-      type: semantic-similarity
-      name: Semantic Similarity
-    dataset:
-      name: sts test 512
-      type: sts-test-512
-    metrics:
-    - type: pearson_cosine
-      value: 0.8096452235754106
-      name: Pearson Cosine
-    - type: spearman_cosine
-      value: 0.816264314810491
-      name: Spearman Cosine
-    - type: pearson_manhattan
-      value: 0.8180021560255247
-      name: Pearson Manhattan
-    - type: spearman_manhattan
-      value: 0.8165486306356095
-      name: Spearman Manhattan
-    - type: pearson_euclidean
-      value: 0.8173829404008947
-      name: Pearson Euclidean
-    - type: spearman_euclidean
-      value: 0.8158592878546184
-      name: Spearman Euclidean
-    - type: pearson_dot
-      value: 0.8059176831913651
-      name: Pearson Dot
-    - type: spearman_dot
-      value: 0.8088972406630007
-      name: Spearman Dot
-    - type: pearson_max
-      value: 0.8180021560255247
-      name: Pearson Max
-    - type: spearman_max
-      value: 0.8165486306356095
-      name: Spearman Max
-  - task:
-      type: semantic-similarity
-      name: Semantic Similarity
-    dataset:
-      name: sts test 256
-      type: sts-test-256
-    metrics:
-    - type: pearson_cosine
-      value: 0.8070921035712145
-      name: Pearson Cosine
-    - type: spearman_cosine
-      value: 0.8150266310280979
-      name: Spearman Cosine
-    - type: pearson_manhattan
-      value: 0.818409081545237
-      name: Pearson Manhattan
-    - type: spearman_manhattan
-      value: 0.8167245415653657
-      name: Spearman Manhattan
-    - type: pearson_euclidean
-      value: 0.8176811220335696
-      name: Pearson Euclidean
-    - type: spearman_euclidean
-      value: 0.8158894222194816
-      name: Spearman Euclidean
-    - type: pearson_dot
-      value: 0.795483328805793
-      name: Pearson Dot
-    - type: spearman_dot
-      value: 0.7956062163122977
-      name: Spearman Dot
-    - type: pearson_max
-      value: 0.818409081545237
-      name: Pearson Max
-    - type: spearman_max
-      value: 0.8167245415653657
-      name: Spearman Max
-  - task:
-      type: semantic-similarity
-      name: Semantic Similarity
-    dataset:
-      name: sts test 128
-      type: sts-test-128
-    metrics:
-    - type: pearson_cosine
-      value: 0.7974039089035316
-      name: Pearson Cosine
-    - type: spearman_cosine
-      value: 0.8093067652791092
-      name: Spearman Cosine
-    - type: pearson_manhattan
-      value: 0.8125792968401813
-      name: Pearson Manhattan
-    - type: spearman_manhattan
-      value: 0.8121486514324944
-      name: Spearman Manhattan
-    - type: pearson_euclidean
-      value: 0.8119102513178551
-      name: Pearson Euclidean
-    - type: spearman_euclidean
-      value: 0.811152531425261
-      name: Spearman Euclidean
-    - type: pearson_dot
-      value: 0.7739555890021923
-      name: Pearson Dot
-    - type: spearman_dot
-      value: 0.770072655568691
-      name: Spearman Dot
-    - type: pearson_max
-      value: 0.8125792968401813
-      name: Pearson Max
-    - type: spearman_max
-      value: 0.8121486514324944
-      name: Spearman Max
-  - task:
-      type: semantic-similarity
-      name: Semantic Similarity
-    dataset:
-      name: sts test 64
-      type: sts-test-64
-    metrics:
-    - type: pearson_cosine
-      value: 0.7873069617689994
-      name: Pearson Cosine
-    - type: spearman_cosine
-      value: 0.8024994399645912
-      name: Spearman Cosine
-    - type: pearson_manhattan
-      value: 0.8048161563115213
-      name: Pearson Manhattan
-    - type: spearman_manhattan
-      value: 0.8031972835914969
-      name: Spearman Manhattan
-    - type: pearson_euclidean
-      value: 0.8060416893207731
-      name: Pearson Euclidean
-    - type: spearman_euclidean
-      value: 0.8041515980374414
-      name: Spearman Euclidean
-    - type: pearson_dot
-      value: 0.747911221220991
-      name: Pearson Dot
-    - type: spearman_dot
-      value: 0.7386011869481828
-      name: Spearman Dot
-    - type: pearson_max
-      value: 0.8060416893207731
-      name: Pearson Max
-    - type: spearman_max
-      value: 0.8041515980374414
-      name: Spearman Max
----
-
-# SentenceTransformer based on aari1995/gbert-large-2-cls-nlisim
-
-This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [aari1995/gbert-large-2-cls-nlisim](https://huggingface.co./aari1995/gbert-large-2-cls-nlisim) on the [PhilipMay/stsb_multi_mt](https://huggingface.co./datasets/PhilipMay/stsb_multi_mt) dataset. It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
-
-## Model Details
-
-### Model Description
-- **Model Type:** Sentence Transformer
-- **Base model:** [aari1995/gbert-large-2-cls-nlisim](https://huggingface.co./aari1995/gbert-large-2-cls-nlisim) <!-- at revision fb515aefe7a575165dcaa62db3f77a09642ebe64 -->
-- **Maximum Sequence Length:** 8192 tokens
-- **Output Dimensionality:** 1024 tokens
-- **Similarity Function:** Cosine Similarity
-- **Training Dataset:**
-    - [PhilipMay/stsb_multi_mt](https://huggingface.co./datasets/PhilipMay/stsb_multi_mt)
-- **Languages:** de, en, es, fr, it, nl, pl, pt, ru, zh
-<!-- - **License:** Unknown -->
-
-### Model Sources
-
-- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
-- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
-- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co./models?library=sentence-transformers)
-
-### Full Model Architecture
-
-```
-SentenceTransformer(
-  (0): Transformer({'max_seq_length': 8192, 'do_lower_case': False}) with Transformer model: JinaBertModel 
-  (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
-)
-```
-
-## Usage
-
-### Direct Usage (Sentence Transformers)
-
-First install the Sentence Transformers library:
-
-```bash
-pip install -U sentence-transformers
-```
-
-Then you can load this model and run inference.
-```python
-from sentence_transformers import SentenceTransformer
-
-# Download from the 🤗 Hub
-model = SentenceTransformer("aari1995/gbert-large-2-cls-pawsx-nli-sts")
-# Run inference
-sentences = [
-    'Zwei Frauen laufen.',
-    'Frauen laufen.',
-    'Die Frau prüft die Augen des Mannes.',
-]
-embeddings = model.encode(sentences)
-print(embeddings.shape)
-# [3, 1024]
-
-# Get the similarity scores for the embeddings
-similarities = model.similarity(embeddings, embeddings)
-print(similarities.shape)
-# [3, 3]
-```
-
-<!--
-### Direct Usage (Transformers)
-
-<details><summary>Click to see the direct usage in Transformers</summary>
-
-</details>
--->
-
-<!--
-### Downstream Usage (Sentence Transformers)
-
-You can finetune this model on your own dataset.
-
-<details><summary>Click to expand</summary>
-
-</details>
--->
-
-<!--
-### Out-of-Scope Use
-
-*List how the model may foreseeably be misused and address what users ought not to do with the model.*
--->
-
-## Evaluation
-
-### Metrics
-
-#### Semantic Similarity
-* Dataset: `sts-dev-1024`
-* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
-
-| Metric              | Value      |
-|:--------------------|:-----------|
-| pearson_cosine      | 0.8418     |
-| **spearman_cosine** | **0.8453** |
-| pearson_manhattan   | 0.8419     |
-| spearman_manhattan  | 0.845      |
-| pearson_euclidean   | 0.8423     |
-| spearman_euclidean  | 0.8453     |
-| pearson_dot         | 0.8417     |
-| spearman_dot        | 0.8451     |
-| pearson_max         | 0.8423     |
-| spearman_max        | 0.8453     |
-
-#### Semantic Similarity
-* Dataset: `sts-dev-768`
-* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
-
-| Metric              | Value      |
-|:--------------------|:-----------|
-| pearson_cosine      | 0.8418     |
-| **spearman_cosine** | **0.8454** |
-| pearson_manhattan   | 0.8419     |
-| spearman_manhattan  | 0.8448     |
-| pearson_euclidean   | 0.8423     |
-| spearman_euclidean  | 0.8452     |
-| pearson_dot         | 0.8418     |
-| spearman_dot        | 0.8447     |
-| pearson_max         | 0.8423     |
-| spearman_max        | 0.8454     |
-
-#### Semantic Similarity
-* Dataset: `sts-dev-512`
-* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
-
-| Metric              | Value      |
-|:--------------------|:-----------|
-| pearson_cosine      | 0.8395     |
-| **spearman_cosine** | **0.8438** |
-| pearson_manhattan   | 0.842      |
-| spearman_manhattan  | 0.8447     |
-| pearson_euclidean   | 0.8423     |
-| spearman_euclidean  | 0.8449     |
-| pearson_dot         | 0.8358     |
-| spearman_dot        | 0.838      |
-| pearson_max         | 0.8423     |
-| spearman_max        | 0.8449     |
-
-#### Semantic Similarity
-* Dataset: `sts-dev-256`
-* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
-
-| Metric              | Value      |
-|:--------------------|:-----------|
-| pearson_cosine      | 0.8339     |
-| **spearman_cosine** | **0.8392** |
-| pearson_manhattan   | 0.838      |
-| spearman_manhattan  | 0.8399     |
-| pearson_euclidean   | 0.8389     |
-| spearman_euclidean  | 0.8405     |
-| pearson_dot         | 0.8231     |
-| spearman_dot        | 0.8243     |
-| pearson_max         | 0.8389     |
-| spearman_max        | 0.8405     |
-
-#### Semantic Similarity
-* Dataset: `sts-dev-128`
-* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
-
-| Metric              | Value      |
-|:--------------------|:-----------|
-| pearson_cosine      | 0.8254     |
-| **spearman_cosine** | **0.8336** |
-| pearson_manhattan   | 0.8342     |
-| spearman_manhattan  | 0.8344     |
-| pearson_euclidean   | 0.8355     |
-| spearman_euclidean  | 0.8359     |
-| pearson_dot         | 0.8035     |
-| spearman_dot        | 0.805      |
-| pearson_max         | 0.8355     |
-| spearman_max        | 0.8359     |
-
-#### Semantic Similarity
-* Dataset: `sts-dev-64`
-* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
-
-| Metric              | Value      |
-|:--------------------|:-----------|
-| pearson_cosine      | 0.8151     |
-| **spearman_cosine** | **0.8266** |
-| pearson_manhattan   | 0.8242     |
-| spearman_manhattan  | 0.8239     |
-| pearson_euclidean   | 0.8275     |
-| spearman_euclidean  | 0.8271     |
-| pearson_dot         | 0.7774     |
-| spearman_dot        | 0.779      |
-| pearson_max         | 0.8275     |
-| spearman_max        | 0.8271     |
-
-#### Semantic Similarity
-* Dataset: `sts-test-1024`
-* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
-
-| Metric              | Value      |
-|:--------------------|:-----------|
-| pearson_cosine      | 0.8131     |
-| **spearman_cosine** | **0.8189** |
-| pearson_manhattan   | 0.8209     |
-| spearman_manhattan  | 0.8195     |
-| pearson_euclidean   | 0.8203     |
-| spearman_euclidean  | 0.8189     |
-| pearson_dot         | 0.8128     |
-| spearman_dot        | 0.8186     |
-| pearson_max         | 0.8209     |
-| spearman_max        | 0.8195     |
-
-#### Semantic Similarity
-* Dataset: `sts-test-768`
-* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
-
-| Metric              | Value      |
-|:--------------------|:-----------|
-| pearson_cosine      | 0.8122     |
-| **spearman_cosine** | **0.8183** |
-| pearson_manhattan   | 0.8206     |
-| spearman_manhattan  | 0.819      |
-| pearson_euclidean   | 0.8197     |
-| spearman_euclidean  | 0.8183     |
-| pearson_dot         | 0.8107     |
-| spearman_dot        | 0.8149     |
-| pearson_max         | 0.8206     |
-| spearman_max        | 0.819      |
-
-#### Semantic Similarity
-* Dataset: `sts-test-512`
-* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
-
-| Metric              | Value      |
-|:--------------------|:-----------|
-| pearson_cosine      | 0.8096     |
-| **spearman_cosine** | **0.8163** |
-| pearson_manhattan   | 0.818      |
-| spearman_manhattan  | 0.8165     |
-| pearson_euclidean   | 0.8174     |
-| spearman_euclidean  | 0.8159     |
-| pearson_dot         | 0.8059     |
-| spearman_dot        | 0.8089     |
-| pearson_max         | 0.818      |
-| spearman_max        | 0.8165     |
-
-#### Semantic Similarity
-* Dataset: `sts-test-256`
-* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
-
-| Metric              | Value     |
-|:--------------------|:----------|
-| pearson_cosine      | 0.8071    |
-| **spearman_cosine** | **0.815** |
-| pearson_manhattan   | 0.8184    |
-| spearman_manhattan  | 0.8167    |
-| pearson_euclidean   | 0.8177    |
-| spearman_euclidean  | 0.8159    |
-| pearson_dot         | 0.7955    |
-| spearman_dot        | 0.7956    |
-| pearson_max         | 0.8184    |
-| spearman_max        | 0.8167    |
-
-#### Semantic Similarity
-* Dataset: `sts-test-128`
-* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
-
-| Metric              | Value      |
-|:--------------------|:-----------|
-| pearson_cosine      | 0.7974     |
-| **spearman_cosine** | **0.8093** |
-| pearson_manhattan   | 0.8126     |
-| spearman_manhattan  | 0.8121     |
-| pearson_euclidean   | 0.8119     |
-| spearman_euclidean  | 0.8112     |
-| pearson_dot         | 0.774      |
-| spearman_dot        | 0.7701     |
-| pearson_max         | 0.8126     |
-| spearman_max        | 0.8121     |
-
-#### Semantic Similarity
-* Dataset: `sts-test-64`
-* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
-
-| Metric              | Value      |
-|:--------------------|:-----------|
-| pearson_cosine      | 0.7873     |
-| **spearman_cosine** | **0.8025** |
-| pearson_manhattan   | 0.8048     |
-| spearman_manhattan  | 0.8032     |
-| pearson_euclidean   | 0.806      |
-| spearman_euclidean  | 0.8042     |
-| pearson_dot         | 0.7479     |
-| spearman_dot        | 0.7386     |
-| pearson_max         | 0.806      |
-| spearman_max        | 0.8042     |
-
-<!--
-## Bias, Risks and Limitations
-
-*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
--->
-
-<!--
-### Recommendations
-
-*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
--->
-
-## Training Details
-
-### Training Dataset
-
-#### PhilipMay/stsb_multi_mt
-
-* Dataset: [PhilipMay/stsb_multi_mt](https://huggingface.co./datasets/PhilipMay/stsb_multi_mt) at [3acaa3d](https://huggingface.co./datasets/PhilipMay/stsb_multi_mt/tree/3acaa3dd8c91649e0b8e627ffad891f059e47c8c)
-* Size: 22,996 training samples
-* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
-* Approximate statistics based on the first 1000 samples:
-  |         | sentence1                                                                         | sentence2                                                                         | score                                                          |
-  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
-  | type    | string                                                                            | string                                                                            | float                                                          |
-  | details | <ul><li>min: 6 tokens</li><li>mean: 18.13 tokens</li><li>max: 65 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 18.25 tokens</li><li>max: 90 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.54</li><li>max: 1.0</li></ul> |
-* Samples:
-  | sentence1                                                                | sentence2                                                                        | score                           |
-  |:-------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:--------------------------------|
-  | <code>schütze wegen mordes an schwarzem us-jugendlichen angeklagt</code> | <code>gedanken zu den rassenbeziehungen unter einem schwarzen präsidenten</code> | <code>0.1599999964237213</code> |
-  | <code>fußballspieler kicken einen fußball in das tor.</code>             | <code>Ein Fußballspieler schießt ein Tor.</code>                                 | <code>0.7599999904632568</code> |
-  | <code>obama lockert abschiebungsregeln für junge einwanderer</code>      | <code>usa lockert abschiebebestimmungen für jugendliche: napolitano</code>       | <code>0.800000011920929</code>  |
-* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
-  ```json
-  {
-      "loss": "CosineSimilarityLoss",
-      "matryoshka_dims": [
-          1024,
-          768,
-          512,
-          256,
-          128,
-          64
-      ],
-      "matryoshka_weights": [
-          1,
-          1,
-          1,
-          1,
-          1,
-          1
-      ],
-      "n_dims_per_step": -1
-  }
-  ```
-
-### Evaluation Dataset
-
-#### PhilipMay/stsb_multi_mt
-
-* Dataset: [PhilipMay/stsb_multi_mt](https://huggingface.co./datasets/PhilipMay/stsb_multi_mt) at [3acaa3d](https://huggingface.co./datasets/PhilipMay/stsb_multi_mt/tree/3acaa3dd8c91649e0b8e627ffad891f059e47c8c)
-* Size: 1,500 evaluation samples
-* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
-* Approximate statistics based on the first 1000 samples:
-  |         | sentence1                                                                         | sentence2                                                                         | score                                                          |
-  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
-  | type    | string                                                                            | string                                                                            | float                                                          |
-  | details | <ul><li>min: 5 tokens</li><li>mean: 16.54 tokens</li><li>max: 53 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 16.53 tokens</li><li>max: 47 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.47</li><li>max: 1.0</li></ul> |
-* Samples:
-  | sentence1                                                    | sentence2                                                  | score                          |
-  |:-------------------------------------------------------------|:-----------------------------------------------------------|:-------------------------------|
-  | <code>Ein Mann mit einem Schutzhelm tanzt.</code>            | <code>Ein Mann mit einem Schutzhelm tanzt.</code>          | <code>1.0</code>               |
-  | <code>Ein kleines Kind reitet auf einem Pferd.</code>        | <code>Ein Kind reitet auf einem Pferd.</code>              | <code>0.949999988079071</code> |
-  | <code>Ein Mann verfüttert eine Maus an eine Schlange.</code> | <code>Der Mann füttert die Schlange mit einer Maus.</code> | <code>1.0</code>               |
-* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
-  ```json
-  {
-      "loss": "CosineSimilarityLoss",
-      "matryoshka_dims": [
-          1024,
-          768,
-          512,
-          256,
-          128,
-          64
-      ],
-      "matryoshka_weights": [
-          1,
-          1,
-          1,
-          1,
-          1,
-          1
-      ],
-      "n_dims_per_step": -1
-  }
-  ```
-
-### Training Hyperparameters
-#### Non-Default Hyperparameters
-
-- `eval_strategy`: steps
-- `per_device_train_batch_size`: 4
-- `per_device_eval_batch_size`: 16
-- `learning_rate`: 5e-06
-- `num_train_epochs`: 1
-- `warmup_ratio`: 0.1
-- `bf16`: True
-
-#### All Hyperparameters
-<details><summary>Click to expand</summary>
-
-- `overwrite_output_dir`: False
-- `do_predict`: False
-- `eval_strategy`: steps
-- `prediction_loss_only`: True
-- `per_device_train_batch_size`: 4
-- `per_device_eval_batch_size`: 16
-- `per_gpu_train_batch_size`: None
-- `per_gpu_eval_batch_size`: None
-- `gradient_accumulation_steps`: 1
-- `eval_accumulation_steps`: None
-- `learning_rate`: 5e-06
-- `weight_decay`: 0.0
-- `adam_beta1`: 0.9
-- `adam_beta2`: 0.999
-- `adam_epsilon`: 1e-08
-- `max_grad_norm`: 1.0
-- `num_train_epochs`: 1
-- `max_steps`: -1
-- `lr_scheduler_type`: linear
-- `lr_scheduler_kwargs`: {}
-- `warmup_ratio`: 0.1
-- `warmup_steps`: 0
-- `log_level`: passive
-- `log_level_replica`: warning
-- `log_on_each_node`: True
-- `logging_nan_inf_filter`: True
-- `save_safetensors`: True
-- `save_on_each_node`: False
-- `save_only_model`: False
-- `restore_callback_states_from_checkpoint`: False
-- `no_cuda`: False
-- `use_cpu`: False
-- `use_mps_device`: False
-- `seed`: 42
-- `data_seed`: None
-- `jit_mode_eval`: False
-- `use_ipex`: False
-- `bf16`: True
-- `fp16`: False
-- `fp16_opt_level`: O1
-- `half_precision_backend`: auto
-- `bf16_full_eval`: False
-- `fp16_full_eval`: False
-- `tf32`: None
-- `local_rank`: 0
-- `ddp_backend`: None
-- `tpu_num_cores`: None
-- `tpu_metrics_debug`: False
-- `debug`: []
-- `dataloader_drop_last`: False
-- `dataloader_num_workers`: 0
-- `dataloader_prefetch_factor`: None
-- `past_index`: -1
-- `disable_tqdm`: False
-- `remove_unused_columns`: True
-- `label_names`: None
-- `load_best_model_at_end`: False
-- `ignore_data_skip`: False
-- `fsdp`: []
-- `fsdp_min_num_params`: 0
-- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
-- `fsdp_transformer_layer_cls_to_wrap`: None
-- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
-- `deepspeed`: None
-- `label_smoothing_factor`: 0.0
-- `optim`: adamw_torch
-- `optim_args`: None
-- `adafactor`: False
-- `group_by_length`: False
-- `length_column_name`: length
-- `ddp_find_unused_parameters`: None
-- `ddp_bucket_cap_mb`: None
-- `ddp_broadcast_buffers`: False
-- `dataloader_pin_memory`: True
-- `dataloader_persistent_workers`: False
-- `skip_memory_metrics`: True
-- `use_legacy_prediction_loop`: False
-- `push_to_hub`: False
-- `resume_from_checkpoint`: None
-- `hub_model_id`: None
-- `hub_strategy`: every_save
-- `hub_private_repo`: False
-- `hub_always_push`: False
-- `gradient_checkpointing`: False
-- `gradient_checkpointing_kwargs`: None
-- `include_inputs_for_metrics`: False
-- `eval_do_concat_batches`: True
-- `fp16_backend`: auto
-- `push_to_hub_model_id`: None
-- `push_to_hub_organization`: None
-- `mp_parameters`: 
-- `auto_find_batch_size`: False
-- `full_determinism`: False
-- `torchdynamo`: None
-- `ray_scope`: last
-- `ddp_timeout`: 1800
-- `torch_compile`: False
-- `torch_compile_backend`: None
-- `torch_compile_mode`: None
-- `dispatch_batches`: None
-- `split_batches`: None
-- `include_tokens_per_second`: False
-- `include_num_input_tokens_seen`: False
-- `neftune_noise_alpha`: None
-- `optim_target_modules`: None
-- `batch_eval_metrics`: False
-- `eval_on_start`: False
-- `batch_sampler`: batch_sampler
-- `multi_dataset_batch_sampler`: proportional
-
-</details>
-
-### Training Logs
-| Epoch  | Step | Training Loss | loss   | sts-dev-1024_spearman_cosine | sts-dev-128_spearman_cosine | sts-dev-256_spearman_cosine | sts-dev-512_spearman_cosine | sts-dev-64_spearman_cosine | sts-dev-768_spearman_cosine | sts-test-1024_spearman_cosine | sts-test-128_spearman_cosine | sts-test-256_spearman_cosine | sts-test-512_spearman_cosine | sts-test-64_spearman_cosine | sts-test-768_spearman_cosine |
-|:------:|:----:|:-------------:|:------:|:----------------------------:|:---------------------------:|:---------------------------:|:---------------------------:|:--------------------------:|:---------------------------:|:-----------------------------:|:----------------------------:|:----------------------------:|:----------------------------:|:---------------------------:|:----------------------------:|
-| 0.0174 | 100  | 0.2958        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
-| 0.0348 | 200  | 0.2914        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
-| 0.0522 | 300  | 0.2691        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
-| 0.0696 | 400  | 0.253         | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
-| 0.0870 | 500  | 0.2458        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
-| 0.1044 | 600  | 0.2594        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
-| 0.1218 | 700  | 0.2339        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
-| 0.1392 | 800  | 0.2245        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
-| 0.1565 | 900  | 0.2122        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
-| 0.1739 | 1000 | 0.2369        | 0.2394 | 0.8402                       | 0.8277                      | 0.8352                      | 0.8393                      | 0.8164                     | 0.8404                      | -                             | -                            | -                            | -                            | -                           | -                            |
-| 0.1913 | 1100 | 0.2308        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
-| 0.2087 | 1200 | 0.2292        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
-| 0.2261 | 1300 | 0.2232        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
-| 0.2435 | 1400 | 0.2001        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
-| 0.2609 | 1500 | 0.2139        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
-| 0.2783 | 1600 | 0.1906        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
-| 0.2957 | 1700 | 0.1895        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
-| 0.3131 | 1800 | 0.2011        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
-| 0.3305 | 1900 | 0.1723        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
-| 0.3479 | 2000 | 0.1886        | 0.2340 | 0.8448                       | 0.8321                      | 0.8385                      | 0.8435                      | 0.8233                     | 0.8449                      | -                             | -                            | -                            | -                            | -                           | -                            |
-| 0.3653 | 2100 | 0.1719        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
-| 0.3827 | 2200 | 0.1879        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
-| 0.4001 | 2300 | 0.187         | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
-| 0.4175 | 2400 | 0.1487        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
-| 0.4349 | 2500 | 0.1752        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
-| 0.4523 | 2600 | 0.1475        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
-| 0.4696 | 2700 | 0.1695        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
-| 0.4870 | 2800 | 0.1615        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
-| 0.5044 | 2900 | 0.1558        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
-| 0.5218 | 3000 | 0.1713        | 0.2357 | 0.8457                       | 0.8344                      | 0.8406                      | 0.8447                      | 0.8266                     | 0.8461                      | -                             | -                            | -                            | -                            | -                           | -                            |
-| 0.5392 | 3100 | 0.1556        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
-| 0.5566 | 3200 | 0.1743        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
-| 0.5740 | 3300 | 0.1426        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
-| 0.5914 | 3400 | 0.1519        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
-| 0.6088 | 3500 | 0.1763        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
-| 0.6262 | 3600 | 0.1456        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
-| 0.6436 | 3700 | 0.1649        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
-| 0.6610 | 3800 | 0.1427        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
-| 0.6784 | 3900 | 0.1284        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
-| 0.6958 | 4000 | 0.1533        | 0.2344 | 0.8417                       | 0.8291                      | 0.8357                      | 0.8402                      | 0.8225                     | 0.8421                      | -                             | -                            | -                            | -                            | -                           | -                            |
-| 0.7132 | 4100 | 0.1397        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
-| 0.7306 | 4200 | 0.1505        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
-| 0.7480 | 4300 | 0.1355        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
-| 0.7654 | 4400 | 0.1275        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
-| 0.7827 | 4500 | 0.1599        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
-| 0.8001 | 4600 | 0.1493        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
-| 0.8175 | 4700 | 0.1497        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
-| 0.8349 | 4800 | 0.1492        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
-| 0.8523 | 4900 | 0.1378        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
-| 0.8697 | 5000 | 0.1391        | 0.2362 | 0.8453                       | 0.8336                      | 0.8392                      | 0.8438                      | 0.8266                     | 0.8454                      | -                             | -                            | -                            | -                            | -                           | -                            |
-| 0.8871 | 5100 | 0.1622        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
-| 0.9045 | 5200 | 0.1456        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
-| 0.9219 | 5300 | 0.1367        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
-| 0.9393 | 5400 | 0.1243        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
-| 0.9567 | 5500 | 0.1389        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
-| 0.9741 | 5600 | 0.1338        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
-| 0.9915 | 5700 | 0.1146        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
-| 1.0    | 5749 | -             | -      | -                            | -                           | -                           | -                           | -                          | -                           | 0.8189                        | 0.8093                       | 0.8150                       | 0.8163                       | 0.8025                      | 0.8183                       |
-
-
-### Framework Versions
-- Python: 3.9.16
-- Sentence Transformers: 3.0.0
-- Transformers: 4.42.0.dev0
-- PyTorch: 2.2.2+cu118
-- Accelerate: 0.31.0
-- Datasets: 2.19.1
-- Tokenizers: 0.19.1
-
-## Citation
-
-### BibTeX
-
-#### Sentence Transformers
-```bibtex
-@inproceedings{reimers-2019-sentence-bert,
-    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
-    author = "Reimers, Nils and Gurevych, Iryna",
-    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
-    month = "11",
-    year = "2019",
-    publisher = "Association for Computational Linguistics",
-    url = "https://arxiv.org/abs/1908.10084",
-}
-```
-
-#### MatryoshkaLoss
-```bibtex
-@misc{kusupati2024matryoshka,
-    title={Matryoshka Representation Learning}, 
-    author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
-    year={2024},
-    eprint={2205.13147},
-    archivePrefix={arXiv},
-    primaryClass={cs.LG}
-}
-```
-
-<!--
-## Glossary
-
-*Clearly define terms in order to be accessible across audiences.*
--->
-
-<!--
-## Model Card Authors
-
-*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
--->
-
-<!--
-## Model Card Contact
-
-*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+---
+language:
+- de
+- en
+- es
+- fr
+- it
+- nl
+- pl
+- pt
+- ru
+- zh
+library_name: sentence-transformers
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- dataset_size:10K<n<100K
+- loss:MatryoshkaLoss
+- loss:CosineSimilarityLoss
+base_model: aari1995/gbert-large-2-cls-nlisim
+metrics:
+- pearson_cosine
+- spearman_cosine
+- pearson_manhattan
+- spearman_manhattan
+- pearson_euclidean
+- spearman_euclidean
+- pearson_dot
+- spearman_dot
+- pearson_max
+- spearman_max
+widget:
+- source_sentence: Ein Mann spricht.
+  sentences:
+  - Ein Mann spricht in ein Mikrofon.
+  - Der Mann spielt auf den Tastaturen.
+  - Zwei Mädchen gehen im Ozean spazieren.
+- source_sentence: Eine Flagge weht.
+  sentences:
+  - Die Flagge bewegte sich in der Luft.
+  - Ein Hund fährt auf einem Skateboard.
+  - Zwei Frauen sitzen in einem Cafe.
+- source_sentence: Ein Mann übt Boxen
+  sentences:
+  - Ein Affe praktiziert Kampfsportarten.
+  - Eine Person faltet ein Blatt Papier.
+  - Eine Frau geht mit ihrem Hund spazieren.
+- source_sentence: Das Tor ist gelb.
+  sentences:
+  - Das Tor ist blau.
+  - Die Frau hält die Hände des Mannes.
+  - NATO-Soldat bei afghanischem Angriff getötet
+- source_sentence: Zwei Frauen laufen.
+  sentences:
+  - Frauen laufen.
+  - Die Frau prüft die Augen des Mannes.
+  - Ein Mann ist auf einem Dach
+pipeline_tag: sentence-similarity
+model-index:
+- name: SentenceTransformer based on aari1995/gbert-large-2-cls-nlisim
+  results:
+  - task:
+      type: semantic-similarity
+      name: Semantic Similarity
+    dataset:
+      name: sts dev 1024
+      type: sts-dev-1024
+    metrics:
+    - type: pearson_cosine
+      value: 0.8417806877288009
+      name: Pearson Cosine
+    - type: spearman_cosine
+      value: 0.8452891310343582
+      name: Spearman Cosine
+    - type: pearson_manhattan
+      value: 0.8418749526406495
+      name: Pearson Manhattan
+    - type: spearman_manhattan
+      value: 0.8450348906331776
+      name: Spearman Manhattan
+    - type: pearson_euclidean
+      value: 0.8422615095001257
+      name: Pearson Euclidean
+    - type: spearman_euclidean
+      value: 0.8453390990427703
+      name: Spearman Euclidean
+    - type: pearson_dot
+      value: 0.8416625079549063
+      name: Pearson Dot
+    - type: spearman_dot
+      value: 0.8450616171323844
+      name: Spearman Dot
+    - type: pearson_max
+      value: 0.8422615095001257
+      name: Pearson Max
+    - type: spearman_max
+      value: 0.8453390990427703
+      name: Spearman Max
+  - task:
+      type: semantic-similarity
+      name: Semantic Similarity
+    dataset:
+      name: sts dev 768
+      type: sts-dev-768
+    metrics:
+    - type: pearson_cosine
+      value: 0.8418107096367227
+      name: Pearson Cosine
+    - type: spearman_cosine
+      value: 0.8453863409322975
+      name: Spearman Cosine
+    - type: pearson_manhattan
+      value: 0.8418527770289471
+      name: Pearson Manhattan
+    - type: spearman_manhattan
+      value: 0.8448328869253576
+      name: Spearman Manhattan
+    - type: pearson_euclidean
+      value: 0.8422791953749277
+      name: Pearson Euclidean
+    - type: spearman_euclidean
+      value: 0.8451547857394669
+      name: Spearman Euclidean
+    - type: pearson_dot
+      value: 0.8417682812591724
+      name: Pearson Dot
+    - type: spearman_dot
+      value: 0.8446927200809794
+      name: Spearman Dot
+    - type: pearson_max
+      value: 0.8422791953749277
+      name: Pearson Max
+    - type: spearman_max
+      value: 0.8453863409322975
+      name: Spearman Max
+  - task:
+      type: semantic-similarity
+      name: Semantic Similarity
+    dataset:
+      name: sts dev 512
+      type: sts-dev-512
+    metrics:
+    - type: pearson_cosine
+      value: 0.8394808864309438
+      name: Pearson Cosine
+    - type: spearman_cosine
+      value: 0.8437551103291275
+      name: Spearman Cosine
+    - type: pearson_manhattan
+      value: 0.8420246416513741
+      name: Pearson Manhattan
+    - type: spearman_manhattan
+      value: 0.8447335398769396
+      name: Spearman Manhattan
+    - type: pearson_euclidean
+      value: 0.8422722079216611
+      name: Pearson Euclidean
+    - type: spearman_euclidean
+      value: 0.8448909261141044
+      name: Spearman Euclidean
+    - type: pearson_dot
+      value: 0.8358204287638725
+      name: Pearson Dot
+    - type: spearman_dot
+      value: 0.8380004733308642
+      name: Spearman Dot
+    - type: pearson_max
+      value: 0.8422722079216611
+      name: Pearson Max
+    - type: spearman_max
+      value: 0.8448909261141044
+      name: Spearman Max
+  - task:
+      type: semantic-similarity
+      name: Semantic Similarity
+    dataset:
+      name: sts dev 256
+      type: sts-dev-256
+    metrics:
+    - type: pearson_cosine
+      value: 0.833879413726309
+      name: Pearson Cosine
+    - type: spearman_cosine
+      value: 0.8392439788855341
+      name: Spearman Cosine
+    - type: pearson_manhattan
+      value: 0.8379618268497928
+      name: Pearson Manhattan
+    - type: spearman_manhattan
+      value: 0.839860826315925
+      name: Spearman Manhattan
+    - type: pearson_euclidean
+      value: 0.838931461279174
+      name: Pearson Euclidean
+    - type: spearman_euclidean
+      value: 0.8404811150299943
+      name: Spearman Euclidean
+    - type: pearson_dot
+      value: 0.8230557648139373
+      name: Pearson Dot
+    - type: spearman_dot
+      value: 0.8242532718299653
+      name: Spearman Dot
+    - type: pearson_max
+      value: 0.838931461279174
+      name: Pearson Max
+    - type: spearman_max
+      value: 0.8404811150299943
+      name: Spearman Max
+  - task:
+      type: semantic-similarity
+      name: Semantic Similarity
+    dataset:
+      name: sts dev 128
+      type: sts-dev-128
+    metrics:
+    - type: pearson_cosine
+      value: 0.8253967606033702
+      name: Pearson Cosine
+    - type: spearman_cosine
+      value: 0.8335750690073012
+      name: Spearman Cosine
+    - type: pearson_manhattan
+      value: 0.8341588626988476
+      name: Pearson Manhattan
+    - type: spearman_manhattan
+      value: 0.8343994326050966
+      name: Spearman Manhattan
+    - type: pearson_euclidean
+      value: 0.8355263623880292
+      name: Pearson Euclidean
+    - type: spearman_euclidean
+      value: 0.8358857095028451
+      name: Spearman Euclidean
+    - type: pearson_dot
+      value: 0.8035163216908426
+      name: Pearson Dot
+    - type: spearman_dot
+      value: 0.8050271037746011
+      name: Spearman Dot
+    - type: pearson_max
+      value: 0.8355263623880292
+      name: Pearson Max
+    - type: spearman_max
+      value: 0.8358857095028451
+      name: Spearman Max
+  - task:
+      type: semantic-similarity
+      name: Semantic Similarity
+    dataset:
+      name: sts dev 64
+      type: sts-dev-64
+    metrics:
+    - type: pearson_cosine
+      value: 0.8150661334039712
+      name: Pearson Cosine
+    - type: spearman_cosine
+      value: 0.8265558538619309
+      name: Spearman Cosine
+    - type: pearson_manhattan
+      value: 0.8241988539394505
+      name: Pearson Manhattan
+    - type: spearman_manhattan
+      value: 0.8238763145175863
+      name: Spearman Manhattan
+    - type: pearson_euclidean
+      value: 0.8274925218859535
+      name: Pearson Euclidean
+    - type: spearman_euclidean
+      value: 0.8270778062044848
+      name: Spearman Euclidean
+    - type: pearson_dot
+      value: 0.7773847317840161
+      name: Pearson Dot
+    - type: spearman_dot
+      value: 0.7790338242936304
+      name: Spearman Dot
+    - type: pearson_max
+      value: 0.8274925218859535
+      name: Pearson Max
+    - type: spearman_max
+      value: 0.8270778062044848
+      name: Spearman Max
+  - task:
+      type: semantic-similarity
+      name: Semantic Similarity
+    dataset:
+      name: sts test 1024
+      type: sts-test-1024
+    metrics:
+    - type: pearson_cosine
+      value: 0.8130772714952826
+      name: Pearson Cosine
+    - type: spearman_cosine
+      value: 0.8188901246173036
+      name: Spearman Cosine
+    - type: pearson_manhattan
+      value: 0.8208715312691268
+      name: Pearson Manhattan
+    - type: spearman_manhattan
+      value: 0.8195095089412118
+      name: Spearman Manhattan
+    - type: pearson_euclidean
+      value: 0.820344720619671
+      name: Pearson Euclidean
+    - type: spearman_euclidean
+      value: 0.8189263018901494
+      name: Spearman Euclidean
+    - type: pearson_dot
+      value: 0.8127924456922464
+      name: Pearson Dot
+    - type: spearman_dot
+      value: 0.8185815083131535
+      name: Spearman Dot
+    - type: pearson_max
+      value: 0.8208715312691268
+      name: Pearson Max
+    - type: spearman_max
+      value: 0.8195095089412118
+      name: Spearman Max
+  - task:
+      type: semantic-similarity
+      name: Semantic Similarity
+    dataset:
+      name: sts test 768
+      type: sts-test-768
+    metrics:
+    - type: pearson_cosine
+      value: 0.8121757739236393
+      name: Pearson Cosine
+    - type: spearman_cosine
+      value: 0.8182913347635533
+      name: Spearman Cosine
+    - type: pearson_manhattan
+      value: 0.820604714791802
+      name: Pearson Manhattan
+    - type: spearman_manhattan
+      value: 0.8190481839997107
+      name: Spearman Manhattan
+    - type: pearson_euclidean
+      value: 0.8197462057663948
+      name: Pearson Euclidean
+    - type: spearman_euclidean
+      value: 0.8183157116237637
+      name: Spearman Euclidean
+    - type: pearson_dot
+      value: 0.8106698462984598
+      name: Pearson Dot
+    - type: spearman_dot
+      value: 0.8148932181769889
+      name: Spearman Dot
+    - type: pearson_max
+      value: 0.820604714791802
+      name: Pearson Max
+    - type: spearman_max
+      value: 0.8190481839997107
+      name: Spearman Max
+  - task:
+      type: semantic-similarity
+      name: Semantic Similarity
+    dataset:
+      name: sts test 512
+      type: sts-test-512
+    metrics:
+    - type: pearson_cosine
+      value: 0.8096452235754106
+      name: Pearson Cosine
+    - type: spearman_cosine
+      value: 0.816264314810491
+      name: Spearman Cosine
+    - type: pearson_manhattan
+      value: 0.8180021560255247
+      name: Pearson Manhattan
+    - type: spearman_manhattan
+      value: 0.8165486306356095
+      name: Spearman Manhattan
+    - type: pearson_euclidean
+      value: 0.8173829404008947
+      name: Pearson Euclidean
+    - type: spearman_euclidean
+      value: 0.8158592878546184
+      name: Spearman Euclidean
+    - type: pearson_dot
+      value: 0.8059176831913651
+      name: Pearson Dot
+    - type: spearman_dot
+      value: 0.8088972406630007
+      name: Spearman Dot
+    - type: pearson_max
+      value: 0.8180021560255247
+      name: Pearson Max
+    - type: spearman_max
+      value: 0.8165486306356095
+      name: Spearman Max
+  - task:
+      type: semantic-similarity
+      name: Semantic Similarity
+    dataset:
+      name: sts test 256
+      type: sts-test-256
+    metrics:
+    - type: pearson_cosine
+      value: 0.8070921035712145
+      name: Pearson Cosine
+    - type: spearman_cosine
+      value: 0.8150266310280979
+      name: Spearman Cosine
+    - type: pearson_manhattan
+      value: 0.818409081545237
+      name: Pearson Manhattan
+    - type: spearman_manhattan
+      value: 0.8167245415653657
+      name: Spearman Manhattan
+    - type: pearson_euclidean
+      value: 0.8176811220335696
+      name: Pearson Euclidean
+    - type: spearman_euclidean
+      value: 0.8158894222194816
+      name: Spearman Euclidean
+    - type: pearson_dot
+      value: 0.795483328805793
+      name: Pearson Dot
+    - type: spearman_dot
+      value: 0.7956062163122977
+      name: Spearman Dot
+    - type: pearson_max
+      value: 0.818409081545237
+      name: Pearson Max
+    - type: spearman_max
+      value: 0.8167245415653657
+      name: Spearman Max
+  - task:
+      type: semantic-similarity
+      name: Semantic Similarity
+    dataset:
+      name: sts test 128
+      type: sts-test-128
+    metrics:
+    - type: pearson_cosine
+      value: 0.7974039089035316
+      name: Pearson Cosine
+    - type: spearman_cosine
+      value: 0.8093067652791092
+      name: Spearman Cosine
+    - type: pearson_manhattan
+      value: 0.8125792968401813
+      name: Pearson Manhattan
+    - type: spearman_manhattan
+      value: 0.8121486514324944
+      name: Spearman Manhattan
+    - type: pearson_euclidean
+      value: 0.8119102513178551
+      name: Pearson Euclidean
+    - type: spearman_euclidean
+      value: 0.811152531425261
+      name: Spearman Euclidean
+    - type: pearson_dot
+      value: 0.7739555890021923
+      name: Pearson Dot
+    - type: spearman_dot
+      value: 0.770072655568691
+      name: Spearman Dot
+    - type: pearson_max
+      value: 0.8125792968401813
+      name: Pearson Max
+    - type: spearman_max
+      value: 0.8121486514324944
+      name: Spearman Max
+  - task:
+      type: semantic-similarity
+      name: Semantic Similarity
+    dataset:
+      name: sts test 64
+      type: sts-test-64
+    metrics:
+    - type: pearson_cosine
+      value: 0.7873069617689994
+      name: Pearson Cosine
+    - type: spearman_cosine
+      value: 0.8024994399645912
+      name: Spearman Cosine
+    - type: pearson_manhattan
+      value: 0.8048161563115213
+      name: Pearson Manhattan
+    - type: spearman_manhattan
+      value: 0.8031972835914969
+      name: Spearman Manhattan
+    - type: pearson_euclidean
+      value: 0.8060416893207731
+      name: Pearson Euclidean
+    - type: spearman_euclidean
+      value: 0.8041515980374414
+      name: Spearman Euclidean
+    - type: pearson_dot
+      value: 0.747911221220991
+      name: Pearson Dot
+    - type: spearman_dot
+      value: 0.7386011869481828
+      name: Spearman Dot
+    - type: pearson_max
+      value: 0.8060416893207731
+      name: Pearson Max
+    - type: spearman_max
+      value: 0.8041515980374414
+      name: Spearman Max
+---
+
+# SentenceTransformer based on aari1995/gbert-large-2-cls-nlisim
+
+POTENTIAL GERMAN_SEMANTIC_V3
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [aari1995/gbert-large-2-cls-nlisim](https://huggingface.co./aari1995/gbert-large-2-cls-nlisim) on the [PhilipMay/stsb_multi_mt](https://huggingface.co./datasets/PhilipMay/stsb_multi_mt) dataset. It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
+
+## Model Details
+
+### Model Description
+- **Model Type:** Sentence Transformer
+- **Base model:** [aari1995/gbert-large-2-cls-nlisim](https://huggingface.co./aari1995/gbert-large-2-cls-nlisim) <!-- at revision fb515aefe7a575165dcaa62db3f77a09642ebe64 -->
+- **Maximum Sequence Length:** 8192 tokens
+- **Output Dimensionality:** 1024 tokens
+- **Similarity Function:** Cosine Similarity
+- **Training Dataset:**
+    - [PhilipMay/stsb_multi_mt](https://huggingface.co./datasets/PhilipMay/stsb_multi_mt)
+- **Languages:** de, en, es, fr, it, nl, pl, pt, ru, zh
+<!-- - **License:** Unknown -->
+
+### Model Sources
+
+- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
+- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
+- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co./models?library=sentence-transformers)
+
+### Full Model Architecture
+
+```
+SentenceTransformer(
+  (0): Transformer({'max_seq_length': 8192, 'do_lower_case': False}) with Transformer model: JinaBertModel 
+  (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+)
+```
+
+## Usage
+
+### Direct Usage (Sentence Transformers)
+
+First install the Sentence Transformers library:
+
+```bash
+pip install -U sentence-transformers
+```
+
+Then you can load this model and run inference.
+```python
+from sentence_transformers import SentenceTransformer
+
+# Download from the 🤗 Hub
+model = SentenceTransformer("aari1995/gbert-large-2-cls-pawsx-nli-sts")
+# Run inference
+sentences = [
+    'Zwei Frauen laufen.',
+    'Frauen laufen.',
+    'Die Frau prüft die Augen des Mannes.',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 1024]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(embeddings, embeddings)
+print(similarities.shape)
+# [3, 3]
+```
+
+<!--
+### Direct Usage (Transformers)
+
+<details><summary>Click to see the direct usage in Transformers</summary>
+
+</details>
+-->
+
+<!--
+### Downstream Usage (Sentence Transformers)
+
+You can finetune this model on your own dataset.
+
+<details><summary>Click to expand</summary>
+
+</details>
+-->
+
+<!--
+### Out-of-Scope Use
+
+*List how the model may foreseeably be misused and address what users ought not to do with the model.*
+-->
+
+## Evaluation
+
+### Metrics
+
+#### Semantic Similarity
+* Dataset: `sts-dev-1024`
+* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| pearson_cosine      | 0.8418     |
+| **spearman_cosine** | **0.8453** |
+| pearson_manhattan   | 0.8419     |
+| spearman_manhattan  | 0.845      |
+| pearson_euclidean   | 0.8423     |
+| spearman_euclidean  | 0.8453     |
+| pearson_dot         | 0.8417     |
+| spearman_dot        | 0.8451     |
+| pearson_max         | 0.8423     |
+| spearman_max        | 0.8453     |
+
+#### Semantic Similarity
+* Dataset: `sts-dev-768`
+* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| pearson_cosine      | 0.8418     |
+| **spearman_cosine** | **0.8454** |
+| pearson_manhattan   | 0.8419     |
+| spearman_manhattan  | 0.8448     |
+| pearson_euclidean   | 0.8423     |
+| spearman_euclidean  | 0.8452     |
+| pearson_dot         | 0.8418     |
+| spearman_dot        | 0.8447     |
+| pearson_max         | 0.8423     |
+| spearman_max        | 0.8454     |
+
+#### Semantic Similarity
+* Dataset: `sts-dev-512`
+* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| pearson_cosine      | 0.8395     |
+| **spearman_cosine** | **0.8438** |
+| pearson_manhattan   | 0.842      |
+| spearman_manhattan  | 0.8447     |
+| pearson_euclidean   | 0.8423     |
+| spearman_euclidean  | 0.8449     |
+| pearson_dot         | 0.8358     |
+| spearman_dot        | 0.838      |
+| pearson_max         | 0.8423     |
+| spearman_max        | 0.8449     |
+
+#### Semantic Similarity
+* Dataset: `sts-dev-256`
+* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| pearson_cosine      | 0.8339     |
+| **spearman_cosine** | **0.8392** |
+| pearson_manhattan   | 0.838      |
+| spearman_manhattan  | 0.8399     |
+| pearson_euclidean   | 0.8389     |
+| spearman_euclidean  | 0.8405     |
+| pearson_dot         | 0.8231     |
+| spearman_dot        | 0.8243     |
+| pearson_max         | 0.8389     |
+| spearman_max        | 0.8405     |
+
+#### Semantic Similarity
+* Dataset: `sts-dev-128`
+* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| pearson_cosine      | 0.8254     |
+| **spearman_cosine** | **0.8336** |
+| pearson_manhattan   | 0.8342     |
+| spearman_manhattan  | 0.8344     |
+| pearson_euclidean   | 0.8355     |
+| spearman_euclidean  | 0.8359     |
+| pearson_dot         | 0.8035     |
+| spearman_dot        | 0.805      |
+| pearson_max         | 0.8355     |
+| spearman_max        | 0.8359     |
+
+#### Semantic Similarity
+* Dataset: `sts-dev-64`
+* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| pearson_cosine      | 0.8151     |
+| **spearman_cosine** | **0.8266** |
+| pearson_manhattan   | 0.8242     |
+| spearman_manhattan  | 0.8239     |
+| pearson_euclidean   | 0.8275     |
+| spearman_euclidean  | 0.8271     |
+| pearson_dot         | 0.7774     |
+| spearman_dot        | 0.779      |
+| pearson_max         | 0.8275     |
+| spearman_max        | 0.8271     |
+
+#### Semantic Similarity
+* Dataset: `sts-test-1024`
+* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| pearson_cosine      | 0.8131     |
+| **spearman_cosine** | **0.8189** |
+| pearson_manhattan   | 0.8209     |
+| spearman_manhattan  | 0.8195     |
+| pearson_euclidean   | 0.8203     |
+| spearman_euclidean  | 0.8189     |
+| pearson_dot         | 0.8128     |
+| spearman_dot        | 0.8186     |
+| pearson_max         | 0.8209     |
+| spearman_max        | 0.8195     |
+
+#### Semantic Similarity
+* Dataset: `sts-test-768`
+* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| pearson_cosine      | 0.8122     |
+| **spearman_cosine** | **0.8183** |
+| pearson_manhattan   | 0.8206     |
+| spearman_manhattan  | 0.819      |
+| pearson_euclidean   | 0.8197     |
+| spearman_euclidean  | 0.8183     |
+| pearson_dot         | 0.8107     |
+| spearman_dot        | 0.8149     |
+| pearson_max         | 0.8206     |
+| spearman_max        | 0.819      |
+
+#### Semantic Similarity
+* Dataset: `sts-test-512`
+* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| pearson_cosine      | 0.8096     |
+| **spearman_cosine** | **0.8163** |
+| pearson_manhattan   | 0.818      |
+| spearman_manhattan  | 0.8165     |
+| pearson_euclidean   | 0.8174     |
+| spearman_euclidean  | 0.8159     |
+| pearson_dot         | 0.8059     |
+| spearman_dot        | 0.8089     |
+| pearson_max         | 0.818      |
+| spearman_max        | 0.8165     |
+
+#### Semantic Similarity
+* Dataset: `sts-test-256`
+* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
+
+| Metric              | Value     |
+|:--------------------|:----------|
+| pearson_cosine      | 0.8071    |
+| **spearman_cosine** | **0.815** |
+| pearson_manhattan   | 0.8184    |
+| spearman_manhattan  | 0.8167    |
+| pearson_euclidean   | 0.8177    |
+| spearman_euclidean  | 0.8159    |
+| pearson_dot         | 0.7955    |
+| spearman_dot        | 0.7956    |
+| pearson_max         | 0.8184    |
+| spearman_max        | 0.8167    |
+
+#### Semantic Similarity
+* Dataset: `sts-test-128`
+* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| pearson_cosine      | 0.7974     |
+| **spearman_cosine** | **0.8093** |
+| pearson_manhattan   | 0.8126     |
+| spearman_manhattan  | 0.8121     |
+| pearson_euclidean   | 0.8119     |
+| spearman_euclidean  | 0.8112     |
+| pearson_dot         | 0.774      |
+| spearman_dot        | 0.7701     |
+| pearson_max         | 0.8126     |
+| spearman_max        | 0.8121     |
+
+#### Semantic Similarity
+* Dataset: `sts-test-64`
+* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| pearson_cosine      | 0.7873     |
+| **spearman_cosine** | **0.8025** |
+| pearson_manhattan   | 0.8048     |
+| spearman_manhattan  | 0.8032     |
+| pearson_euclidean   | 0.806      |
+| spearman_euclidean  | 0.8042     |
+| pearson_dot         | 0.7479     |
+| spearman_dot        | 0.7386     |
+| pearson_max         | 0.806      |
+| spearman_max        | 0.8042     |
+
+<!--
+## Bias, Risks and Limitations
+
+*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
+-->
+
+<!--
+### Recommendations
+
+*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
+-->
+
+## Training Details
+
+### Training Dataset
+
+#### PhilipMay/stsb_multi_mt
+
+* Dataset: [PhilipMay/stsb_multi_mt](https://huggingface.co./datasets/PhilipMay/stsb_multi_mt) at [3acaa3d](https://huggingface.co./datasets/PhilipMay/stsb_multi_mt/tree/3acaa3dd8c91649e0b8e627ffad891f059e47c8c)
+* Size: 22,996 training samples
+* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | sentence1                                                                         | sentence2                                                                         | score                                                          |
+  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
+  | type    | string                                                                            | string                                                                            | float                                                          |
+  | details | <ul><li>min: 6 tokens</li><li>mean: 18.13 tokens</li><li>max: 65 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 18.25 tokens</li><li>max: 90 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.54</li><li>max: 1.0</li></ul> |
+* Samples:
+  | sentence1                                                                | sentence2                                                                        | score                           |
+  |:-------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:--------------------------------|
+  | <code>schütze wegen mordes an schwarzem us-jugendlichen angeklagt</code> | <code>gedanken zu den rassenbeziehungen unter einem schwarzen präsidenten</code> | <code>0.1599999964237213</code> |
+  | <code>fußballspieler kicken einen fußball in das tor.</code>             | <code>Ein Fußballspieler schießt ein Tor.</code>                                 | <code>0.7599999904632568</code> |
+  | <code>obama lockert abschiebungsregeln für junge einwanderer</code>      | <code>usa lockert abschiebebestimmungen für jugendliche: napolitano</code>       | <code>0.800000011920929</code>  |
+* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
+  ```json
+  {
+      "loss": "CosineSimilarityLoss",
+      "matryoshka_dims": [
+          1024,
+          768,
+          512,
+          256,
+          128,
+          64
+      ],
+      "matryoshka_weights": [
+          1,
+          1,
+          1,
+          1,
+          1,
+          1
+      ],
+      "n_dims_per_step": -1
+  }
+  ```
+
+### Evaluation Dataset
+
+#### PhilipMay/stsb_multi_mt
+
+* Dataset: [PhilipMay/stsb_multi_mt](https://huggingface.co./datasets/PhilipMay/stsb_multi_mt) at [3acaa3d](https://huggingface.co./datasets/PhilipMay/stsb_multi_mt/tree/3acaa3dd8c91649e0b8e627ffad891f059e47c8c)
+* Size: 1,500 evaluation samples
+* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | sentence1                                                                         | sentence2                                                                         | score                                                          |
+  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
+  | type    | string                                                                            | string                                                                            | float                                                          |
+  | details | <ul><li>min: 5 tokens</li><li>mean: 16.54 tokens</li><li>max: 53 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 16.53 tokens</li><li>max: 47 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.47</li><li>max: 1.0</li></ul> |
+* Samples:
+  | sentence1                                                    | sentence2                                                  | score                          |
+  |:-------------------------------------------------------------|:-----------------------------------------------------------|:-------------------------------|
+  | <code>Ein Mann mit einem Schutzhelm tanzt.</code>            | <code>Ein Mann mit einem Schutzhelm tanzt.</code>          | <code>1.0</code>               |
+  | <code>Ein kleines Kind reitet auf einem Pferd.</code>        | <code>Ein Kind reitet auf einem Pferd.</code>              | <code>0.949999988079071</code> |
+  | <code>Ein Mann verfüttert eine Maus an eine Schlange.</code> | <code>Der Mann füttert die Schlange mit einer Maus.</code> | <code>1.0</code>               |
+* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
+  ```json
+  {
+      "loss": "CosineSimilarityLoss",
+      "matryoshka_dims": [
+          1024,
+          768,
+          512,
+          256,
+          128,
+          64
+      ],
+      "matryoshka_weights": [
+          1,
+          1,
+          1,
+          1,
+          1,
+          1
+      ],
+      "n_dims_per_step": -1
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 4
+- `per_device_eval_batch_size`: 16
+- `learning_rate`: 5e-06
+- `num_train_epochs`: 1
+- `warmup_ratio`: 0.1
+- `bf16`: True
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: steps
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 4
+- `per_device_eval_batch_size`: 16
+- `per_gpu_train_batch_size`: None
+- `per_gpu_eval_batch_size`: None
+- `gradient_accumulation_steps`: 1
+- `eval_accumulation_steps`: None
+- `learning_rate`: 5e-06
+- `weight_decay`: 0.0
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `max_grad_norm`: 1.0
+- `num_train_epochs`: 1
+- `max_steps`: -1
+- `lr_scheduler_type`: linear
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.1
+- `warmup_steps`: 0
+- `log_level`: passive
+- `log_level_replica`: warning
+- `log_on_each_node`: True
+- `logging_nan_inf_filter`: True
+- `save_safetensors`: True
+- `save_on_each_node`: False
+- `save_only_model`: False
+- `restore_callback_states_from_checkpoint`: False
+- `no_cuda`: False
+- `use_cpu`: False
+- `use_mps_device`: False
+- `seed`: 42
+- `data_seed`: None
+- `jit_mode_eval`: False
+- `use_ipex`: False
+- `bf16`: True
+- `fp16`: False
+- `fp16_opt_level`: O1
+- `half_precision_backend`: auto
+- `bf16_full_eval`: False
+- `fp16_full_eval`: False
+- `tf32`: None
+- `local_rank`: 0
+- `ddp_backend`: None
+- `tpu_num_cores`: None
+- `tpu_metrics_debug`: False
+- `debug`: []
+- `dataloader_drop_last`: False
+- `dataloader_num_workers`: 0
+- `dataloader_prefetch_factor`: None
+- `past_index`: -1
+- `disable_tqdm`: False
+- `remove_unused_columns`: True
+- `label_names`: None
+- `load_best_model_at_end`: False
+- `ignore_data_skip`: False
+- `fsdp`: []
+- `fsdp_min_num_params`: 0
+- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
+- `fsdp_transformer_layer_cls_to_wrap`: None
+- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
+- `deepspeed`: None
+- `label_smoothing_factor`: 0.0
+- `optim`: adamw_torch
+- `optim_args`: None
+- `adafactor`: False
+- `group_by_length`: False
+- `length_column_name`: length
+- `ddp_find_unused_parameters`: None
+- `ddp_bucket_cap_mb`: None
+- `ddp_broadcast_buffers`: False
+- `dataloader_pin_memory`: True
+- `dataloader_persistent_workers`: False
+- `skip_memory_metrics`: True
+- `use_legacy_prediction_loop`: False
+- `push_to_hub`: False
+- `resume_from_checkpoint`: None
+- `hub_model_id`: None
+- `hub_strategy`: every_save
+- `hub_private_repo`: False
+- `hub_always_push`: False
+- `gradient_checkpointing`: False
+- `gradient_checkpointing_kwargs`: None
+- `include_inputs_for_metrics`: False
+- `eval_do_concat_batches`: True
+- `fp16_backend`: auto
+- `push_to_hub_model_id`: None
+- `push_to_hub_organization`: None
+- `mp_parameters`: 
+- `auto_find_batch_size`: False
+- `full_determinism`: False
+- `torchdynamo`: None
+- `ray_scope`: last
+- `ddp_timeout`: 1800
+- `torch_compile`: False
+- `torch_compile_backend`: None
+- `torch_compile_mode`: None
+- `dispatch_batches`: None
+- `split_batches`: None
+- `include_tokens_per_second`: False
+- `include_num_input_tokens_seen`: False
+- `neftune_noise_alpha`: None
+- `optim_target_modules`: None
+- `batch_eval_metrics`: False
+- `eval_on_start`: False
+- `batch_sampler`: batch_sampler
+- `multi_dataset_batch_sampler`: proportional
+
+</details>
+
+### Training Logs
+| Epoch  | Step | Training Loss | loss   | sts-dev-1024_spearman_cosine | sts-dev-128_spearman_cosine | sts-dev-256_spearman_cosine | sts-dev-512_spearman_cosine | sts-dev-64_spearman_cosine | sts-dev-768_spearman_cosine | sts-test-1024_spearman_cosine | sts-test-128_spearman_cosine | sts-test-256_spearman_cosine | sts-test-512_spearman_cosine | sts-test-64_spearman_cosine | sts-test-768_spearman_cosine |
+|:------:|:----:|:-------------:|:------:|:----------------------------:|:---------------------------:|:---------------------------:|:---------------------------:|:--------------------------:|:---------------------------:|:-----------------------------:|:----------------------------:|:----------------------------:|:----------------------------:|:---------------------------:|:----------------------------:|
+| 0.0174 | 100  | 0.2958        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
+| 0.0348 | 200  | 0.2914        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
+| 0.0522 | 300  | 0.2691        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
+| 0.0696 | 400  | 0.253         | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
+| 0.0870 | 500  | 0.2458        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
+| 0.1044 | 600  | 0.2594        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
+| 0.1218 | 700  | 0.2339        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
+| 0.1392 | 800  | 0.2245        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
+| 0.1565 | 900  | 0.2122        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
+| 0.1739 | 1000 | 0.2369        | 0.2394 | 0.8402                       | 0.8277                      | 0.8352                      | 0.8393                      | 0.8164                     | 0.8404                      | -                             | -                            | -                            | -                            | -                           | -                            |
+| 0.1913 | 1100 | 0.2308        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
+| 0.2087 | 1200 | 0.2292        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
+| 0.2261 | 1300 | 0.2232        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
+| 0.2435 | 1400 | 0.2001        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
+| 0.2609 | 1500 | 0.2139        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
+| 0.2783 | 1600 | 0.1906        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
+| 0.2957 | 1700 | 0.1895        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
+| 0.3131 | 1800 | 0.2011        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
+| 0.3305 | 1900 | 0.1723        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
+| 0.3479 | 2000 | 0.1886        | 0.2340 | 0.8448                       | 0.8321                      | 0.8385                      | 0.8435                      | 0.8233                     | 0.8449                      | -                             | -                            | -                            | -                            | -                           | -                            |
+| 0.3653 | 2100 | 0.1719        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
+| 0.3827 | 2200 | 0.1879        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
+| 0.4001 | 2300 | 0.187         | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
+| 0.4175 | 2400 | 0.1487        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
+| 0.4349 | 2500 | 0.1752        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
+| 0.4523 | 2600 | 0.1475        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
+| 0.4696 | 2700 | 0.1695        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
+| 0.4870 | 2800 | 0.1615        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
+| 0.5044 | 2900 | 0.1558        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
+| 0.5218 | 3000 | 0.1713        | 0.2357 | 0.8457                       | 0.8344                      | 0.8406                      | 0.8447                      | 0.8266                     | 0.8461                      | -                             | -                            | -                            | -                            | -                           | -                            |
+| 0.5392 | 3100 | 0.1556        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
+| 0.5566 | 3200 | 0.1743        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
+| 0.5740 | 3300 | 0.1426        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
+| 0.5914 | 3400 | 0.1519        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
+| 0.6088 | 3500 | 0.1763        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
+| 0.6262 | 3600 | 0.1456        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
+| 0.6436 | 3700 | 0.1649        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
+| 0.6610 | 3800 | 0.1427        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
+| 0.6784 | 3900 | 0.1284        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
+| 0.6958 | 4000 | 0.1533        | 0.2344 | 0.8417                       | 0.8291                      | 0.8357                      | 0.8402                      | 0.8225                     | 0.8421                      | -                             | -                            | -                            | -                            | -                           | -                            |
+| 0.7132 | 4100 | 0.1397        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
+| 0.7306 | 4200 | 0.1505        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
+| 0.7480 | 4300 | 0.1355        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
+| 0.7654 | 4400 | 0.1275        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
+| 0.7827 | 4500 | 0.1599        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
+| 0.8001 | 4600 | 0.1493        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
+| 0.8175 | 4700 | 0.1497        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
+| 0.8349 | 4800 | 0.1492        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
+| 0.8523 | 4900 | 0.1378        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
+| 0.8697 | 5000 | 0.1391        | 0.2362 | 0.8453                       | 0.8336                      | 0.8392                      | 0.8438                      | 0.8266                     | 0.8454                      | -                             | -                            | -                            | -                            | -                           | -                            |
+| 0.8871 | 5100 | 0.1622        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
+| 0.9045 | 5200 | 0.1456        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
+| 0.9219 | 5300 | 0.1367        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
+| 0.9393 | 5400 | 0.1243        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
+| 0.9567 | 5500 | 0.1389        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
+| 0.9741 | 5600 | 0.1338        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
+| 0.9915 | 5700 | 0.1146        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
+| 1.0    | 5749 | -             | -      | -                            | -                           | -                           | -                           | -                          | -                           | 0.8189                        | 0.8093                       | 0.8150                       | 0.8163                       | 0.8025                      | 0.8183                       |
+
+
+### Framework Versions
+- Python: 3.9.16
+- Sentence Transformers: 3.0.0
+- Transformers: 4.42.0.dev0
+- PyTorch: 2.2.2+cu118
+- Accelerate: 0.31.0
+- Datasets: 2.19.1
+- Tokenizers: 0.19.1
+
+## Citation
+
+### BibTeX
+
+#### Sentence Transformers
+```bibtex
+@inproceedings{reimers-2019-sentence-bert,
+    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
+    author = "Reimers, Nils and Gurevych, Iryna",
+    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
+    month = "11",
+    year = "2019",
+    publisher = "Association for Computational Linguistics",
+    url = "https://arxiv.org/abs/1908.10084",
+}
+```
+
+#### MatryoshkaLoss
+```bibtex
+@misc{kusupati2024matryoshka,
+    title={Matryoshka Representation Learning}, 
+    author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
+    year={2024},
+    eprint={2205.13147},
+    archivePrefix={arXiv},
+    primaryClass={cs.LG}
+}
+```
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
 -->
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