Add new SentenceTransformer model

1_Pooling/config.json

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+{
+  "word_embedding_dimension": 768,
+  "pooling_mode_cls_token": false,
+  "pooling_mode_mean_tokens": true,
+  "pooling_mode_max_tokens": false,
+  "pooling_mode_mean_sqrt_len_tokens": false,
+  "pooling_mode_weightedmean_tokens": false,
+  "pooling_mode_lasttoken": false,
+  "include_prompt": true
+}

2_Dense/config.json

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+{"in_features": 768, "out_features": 512, "bias": true, "activation_function": "torch.nn.modules.activation.Tanh"}

2_Dense/model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d8e186727ab1944c63cd981dec7b02acdf05179e3041a112df3a1dc5d5f790cb
+size 1575072

README.md

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+---
+language:
+- bn
+- cs
+- de
+- en
+- et
+- fi
+- fr
+- gu
+- ha
+- hi
+- is
+- ja
+- kk
+- km
+- lt
+- lv
+- pl
+- ps
+- ru
+- ta
+- tr
+- uk
+- xh
+- zh
+- zu
+- ne
+- ro
+- si
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:1327190
+- loss:CoSENTLoss
+base_model: sentence-transformers/distiluse-base-multilingual-cased-v2
+widget:
+- source_sentence: यहाँका केही धार्मिक सम्पदाहरू यस प्रकार रहेका छन्।
+  sentences:
+  - A party works journalists from advertisements about a massive Himalayan post.
+  - Some religious affiliations here remain.
+  - In Spain, the strict opposition of Roman Catholic churches is found to have assumed
+    a marriage similar to male beach wives.
+- source_sentence: '"We can use this discovery to target both the assembly and stability
+    of the capsid, to either prevent the formation of the virus when it infects the
+    host cell, or break it apart after it''s formed," Luque said. "This could facilitate
+    the characterization and identification of antiviral targets for viruses sharing
+    the same icosahedral layout."'
+  sentences:
+  - FC inter have today released Shefki Kuqi from the club's representative team coach
+    duties.
+  - '"Wir können diese Entdeckung nutzen, um sowohl die Montage als auch die Stabilität
+    des Kapsids anzustreben, um entweder die Bildung des Virus zu verhindern, wenn
+    es die Wirtszelle infiziert oder nach seiner Bildung auseinanderbricht", sagte
+    Luque. "Dies könnte die Charakterisierung und Identifizierung von antiviralen
+    Zielen für Viren erleichtern, die das gleiche ikosaedrische Layout teilen".'
+  - Quellen sagen, Jones sei „wütend“, als das goldene Mädchen des Fernsehens bei
+    einem angespannten Treffen am Dienstag im Hauptquartier seines Geschäftsimperiums
+    in Marlow, Buckinghamshire, zugab, dass ihre neuen Deals - im Wert von bis zu
+    1,5 Millionen Pfund - bedeuteten, dass sie nicht mehr genug Zeit hatte, sich ihrer
+    Hausbekleidungs- und Zubehörmarke Truly zu widmen.
+- source_sentence: He possesses a pistol with silver bullets for protection from vampires
+    and werewolves.
+  sentences:
+  - Er besitzt eine Pistole mit silbernen Kugeln zum Schutz vor Vampiren und Werwölfen.
+  - Bibimbap umfasst Reis, Spinat, Rettich, Bohnensprossen.
+  - BSAC profitierte auch von den großen, aber nicht unbeschränkten persönlichen Vermögen
+    von Rhodos und Beit vor ihrem Tod.
+- source_sentence: To the west of the Badger Head Inlier is the Port Sorell Formation,
+    a tectonic mélange of marine sediments and dolerite.
+  sentences:
+  - Er brennt einen Speer und brennt Flammen aus seinem Mund, wenn er wütend ist.
+  - Westlich des Badger Head Inlier befindet sich die Port Sorell Formation, eine
+    tektonische Mischung aus Sedimenten und Dolerit.
+  - Public Lynching and Mob Violence under Modi Government
+- source_sentence: Garnizoana otomană se retrage în sudul Dunării, iar după 164 de
+    ani cetatea intră din nou sub stăpânirea europenilor.
+  sentences:
+  - This is because, once again, we have taken into account the fact that we have
+    adopted a large number of legislative proposals.
+  - Helsinki University ranks 75th among universities for 2010.
+  - Ottoman garnisoana is withdrawing into the south of the Danube and, after 164
+    years, it is once again under the control of Europeans.
+datasets:
+- RicardoRei/wmt-da-human-evaluation
+- wmt/wmt20_mlqe_task1
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+metrics:
+- pearson_cosine
+- spearman_cosine
+model-index:
+- name: SentenceTransformer based on sentence-transformers/distiluse-base-multilingual-cased-v2
+  results:
+  - task:
+      type: semantic-similarity
+      name: Semantic Similarity
+    dataset:
+      name: sts eval
+      type: sts-eval
+    metrics:
+    - type: pearson_cosine
+      value: 0.42415369784945883
+      name: Pearson Cosine
+    - type: spearman_cosine
+      value: 0.4175469519194782
+      name: Spearman Cosine
+    - type: pearson_cosine
+      value: 0.0772713008408403
+      name: Pearson Cosine
+    - type: spearman_cosine
+      value: 0.13050905562438264
+      name: Spearman Cosine
+    - type: pearson_cosine
+      value: 0.16731845692612535
+      name: Pearson Cosine
+    - type: spearman_cosine
+      value: 0.18366199919315862
+      name: Spearman Cosine
+    - type: pearson_cosine
+      value: 0.3567214608388243
+      name: Pearson Cosine
+    - type: spearman_cosine
+      value: 0.3656734148567112
+      name: Spearman Cosine
+    - type: pearson_cosine
+      value: 0.41267092498678554
+      name: Pearson Cosine
+    - type: spearman_cosine
+      value: 0.41036446071667193
+      name: Spearman Cosine
+    - type: pearson_cosine
+      value: 0.5254563854630899
+      name: Pearson Cosine
+    - type: spearman_cosine
+      value: 0.4785530551765603
+      name: Spearman Cosine
+    - type: pearson_cosine
+      value: 0.31194241573567016
+      name: Pearson Cosine
+    - type: spearman_cosine
+      value: 0.2814160300891252
+      name: Spearman Cosine
+  - task:
+      type: semantic-similarity
+      name: Semantic Similarity
+    dataset:
+      name: sts test
+      type: sts-test
+    metrics:
+    - type: pearson_cosine
+      value: 0.4253603788235729
+      name: Pearson Cosine
+    - type: spearman_cosine
+      value: 0.4166117661445095
+      name: Spearman Cosine
+    - type: pearson_cosine
+      value: 0.022187134575214738
+      name: Pearson Cosine
+    - type: spearman_cosine
+      value: 0.04647559130832398
+      name: Spearman Cosine
+    - type: pearson_cosine
+      value: 0.15979577569463932
+      name: Pearson Cosine
+    - type: spearman_cosine
+      value: 0.2074497419832692
+      name: Spearman Cosine
+    - type: pearson_cosine
+      value: 0.3698928748443983
+      name: Pearson Cosine
+    - type: spearman_cosine
+      value: 0.3757690724227716
+      name: Spearman Cosine
+    - type: pearson_cosine
+      value: 0.44937864470538347
+      name: Pearson Cosine
+    - type: spearman_cosine
+      value: 0.45866193737582717
+      name: Spearman Cosine
+    - type: pearson_cosine
+      value: 0.4466389646053608
+      name: Pearson Cosine
+    - type: spearman_cosine
+      value: 0.4158920394678395
+      name: Spearman Cosine
+    - type: pearson_cosine
+      value: 0.33243289478987115
+      name: Pearson Cosine
+    - type: spearman_cosine
+      value: 0.2806845193699054
+      name: Spearman Cosine
+---
+
+# SentenceTransformer based on sentence-transformers/distiluse-base-multilingual-cased-v2
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/distiluse-base-multilingual-cased-v2](https://huggingface.co/sentence-transformers/distiluse-base-multilingual-cased-v2) on the [wmt_da](https://huggingface.co/datasets/RicardoRei/wmt-da-human-evaluation), [mlqe_en_de](https://huggingface.co/datasets/wmt/wmt20_mlqe_task1), [mlqe_en_zh](https://huggingface.co/datasets/wmt/wmt20_mlqe_task1), [mlqe_et_en](https://huggingface.co/datasets/wmt/wmt20_mlqe_task1), [mlqe_ne_en](https://huggingface.co/datasets/wmt/wmt20_mlqe_task1), [mlqe_ro_en](https://huggingface.co/datasets/wmt/wmt20_mlqe_task1) and [mlqe_si_en](https://huggingface.co/datasets/wmt/wmt20_mlqe_task1) datasets. It maps sentences & paragraphs to a 512-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:** [sentence-transformers/distiluse-base-multilingual-cased-v2](https://huggingface.co/sentence-transformers/distiluse-base-multilingual-cased-v2) <!-- at revision dad0fa1ee4fa6e982d3adbce87c73c02e6aee838 -->
+- **Maximum Sequence Length:** 128 tokens
+- **Output Dimensionality:** 512 dimensions
+- **Similarity Function:** Cosine Similarity
+- **Training Datasets:**
+    - [wmt_da](https://huggingface.co/datasets/RicardoRei/wmt-da-human-evaluation)
+    - [mlqe_en_de](https://huggingface.co/datasets/wmt/wmt20_mlqe_task1)
+    - [mlqe_en_zh](https://huggingface.co/datasets/wmt/wmt20_mlqe_task1)
+    - [mlqe_et_en](https://huggingface.co/datasets/wmt/wmt20_mlqe_task1)
+    - [mlqe_ne_en](https://huggingface.co/datasets/wmt/wmt20_mlqe_task1)
+    - [mlqe_ro_en](https://huggingface.co/datasets/wmt/wmt20_mlqe_task1)
+    - [mlqe_si_en](https://huggingface.co/datasets/wmt/wmt20_mlqe_task1)
+- **Languages:** bn, cs, de, en, et, fi, fr, gu, ha, hi, is, ja, kk, km, lt, lv, pl, ps, ru, ta, tr, uk, xh, zh, zu, ne, ro, si
+<!-- - **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': 128, 'do_lower_case': False}) with Transformer model: DistilBertModel 
+  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+  (2): Dense({'in_features': 768, 'out_features': 512, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'})
+)
+```
+
+## 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("RomainDarous/distiluse-base-multilingual-cased-v2-sts")
+# Run inference
+sentences = [
+    'Garnizoana otomană se retrage în sudul Dunării, iar după 164 de ani cetatea intră din nou sub stăpânirea europenilor.',
+    'Ottoman garnisoana is withdrawing into the south of the Danube and, after 164 years, it is once again under the control of Europeans.',
+    'This is because, once again, we have taken into account the fact that we have adopted a large number of legislative proposals.',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 512]
+
+# 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
+
+* Datasets: `sts-eval`, `sts-test`, `sts-test`, `sts-test`, `sts-test`, `sts-test`, `sts-test` and `sts-test`
+* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
+
+| Metric              | sts-eval   | sts-test   |
+|:--------------------|:-----------|:-----------|
+| pearson_cosine      | 0.4242     | 0.3324     |
+| **spearman_cosine** | **0.4175** | **0.2807** |
+
+#### Semantic Similarity
+
+* Dataset: `sts-eval`
+* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| pearson_cosine      | 0.0773     |
+| **spearman_cosine** | **0.1305** |
+
+#### Semantic Similarity
+
+* Dataset: `sts-eval`
+* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| pearson_cosine      | 0.1673     |
+| **spearman_cosine** | **0.1837** |
+
+#### Semantic Similarity
+
+* Dataset: `sts-eval`
+* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| pearson_cosine      | 0.3567     |
+| **spearman_cosine** | **0.3657** |
+
+#### Semantic Similarity
+
+* Dataset: `sts-eval`
+* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| pearson_cosine      | 0.4127     |
+| **spearman_cosine** | **0.4104** |
+
+#### Semantic Similarity
+
+* Dataset: `sts-eval`
+* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| pearson_cosine      | 0.5255     |
+| **spearman_cosine** | **0.4786** |
+
+#### Semantic Similarity
+
+* Dataset: `sts-eval`
+* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| pearson_cosine      | 0.3119     |
+| **spearman_cosine** | **0.2814** |
+
+<!--
+## 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 Datasets
+
+#### wmt_da
+
+* Dataset: [wmt_da](https://huggingface.co/datasets/RicardoRei/wmt-da-human-evaluation) at [301de38](https://huggingface.co/datasets/RicardoRei/wmt-da-human-evaluation/tree/301de385bf05b0c00a8f4be74965e186164dd425)
+* Size: 1,285,190 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: 4 tokens</li><li>mean: 37.09 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 37.12 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.7</li><li>max: 1.0</li></ul> |
+* Samples:
+  | sentence1                                                                                                                                                                                       | sentence2                                                                                                                                                                            | score             |
+  |:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------|
+  | <code>Z dat ÚZIS také vyplývá, že se zastavil úbytek zdravotních sester v nemocnicích.</code>                                                                                                   | <code>The data from the IHIS also shows that the decline of nurses in hospitals has stopped.</code>                                                                                  | <code>0.47</code> |
+  | <code>Я был самым гордым, самым пьяным девственником, которого кто-либо когда-либо видел.</code>                                                                                                | <code>I was the proudest, most drunk virgin anyone had ever seen.</code>                                                                                                             | <code>0.99</code> |
+  | <code>Das Trampolinspringen hat einen gewissen Außenseitercharme, teilweise weil es für das unaufgeklärte Ohr passender für eine Clownsschule als die für die Olympischen Spiele klingt.</code> | <code>The trampoline jumping has some outsider charm, in part because it sounds more appropriate for the unenlightened ear for a clowns school than the one for the Olympics.</code> | <code>0.81</code> |
+* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "pairwise_cos_sim"
+  }
+  ```
+
+#### mlqe_en_de
+
+* Dataset: [mlqe_en_de](https://huggingface.co/datasets/wmt/wmt20_mlqe_task1) at [0783ed2](https://huggingface.co/datasets/wmt/wmt20_mlqe_task1/tree/0783ed2bd75f44835df4ea664f9ccb85812c8563)
+* Size: 7,000 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: 11 tokens</li><li>mean: 23.78 tokens</li><li>max: 44 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 26.51 tokens</li><li>max: 54 tokens</li></ul> | <ul><li>min: 0.06</li><li>mean: 0.86</li><li>max: 1.0</li></ul> |
+* Samples:
+  | sentence1                                                                                                                | sentence2                                                                                                                                 | score                           |
+  |:-------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------|
+  | <code>Early Muslim traders and merchants visited Bengal while traversing the Silk Road in the first millennium.</code>   | <code>Frühe muslimische Händler und Kaufleute besuchten Bengalen, während sie im ersten Jahrtausend die Seidenstraße durchquerten.</code> | <code>0.9233333468437195</code> |
+  | <code>While Fran dissipated shortly after that, the tropical wave progressed into the northeastern Pacific Ocean.</code> | <code>Während Fran kurz danach zerstreute, entwickelte sich die tropische Welle in den nordöstlichen Pazifischen Ozean.</code>            | <code>0.8899999856948853</code> |
+  | <code>Distressed securities include such events as restructurings, recapitalizations, and bankruptcies.</code>           | <code>Zu den belasteten Wertpapieren gehören Restrukturierungen, Rekapitalisierungen und Insolvenzen.</code>                              | <code>0.9300000071525574</code> |
+* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "pairwise_cos_sim"
+  }
+  ```
+
+#### mlqe_en_zh
+
+* Dataset: [mlqe_en_zh](https://huggingface.co/datasets/wmt/wmt20_mlqe_task1) at [0783ed2](https://huggingface.co/datasets/wmt/wmt20_mlqe_task1/tree/0783ed2bd75f44835df4ea664f9ccb85812c8563)
+* Size: 7,000 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: 9 tokens</li><li>mean: 24.09 tokens</li><li>max: 47 tokens</li></ul> | <ul><li>min: 12 tokens</li><li>mean: 29.93 tokens</li><li>max: 74 tokens</li></ul> | <ul><li>min: 0.01</li><li>mean: 0.68</li><li>max: 0.98</li></ul> |
+* Samples:
+  | sentence1                                                                                                                | sentence2                                                     | score                            |
+  |:-------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------|:---------------------------------|
+  | <code>In the late 1980s, the hotel's reputation declined, and it functioned partly as a "backpackers hangout."</code>    | <code>在 20 世纪 80 年代末 ， 这家旅馆的声誉下降了 ， 部分地起到了 "背包吊销" 的作用。</code> | <code>0.40666666626930237</code> |
+  | <code>From 1870 to 1915, 36 million Europeans migrated away from Europe.</code>                                          | <code>从 1870 年到 1915 年 ， 3, 600 万欧洲人从欧洲移民。</code>             | <code>0.8333333730697632</code>  |
+  | <code>In some photos, the footpads did press into the regolith, especially when they moved sideways at touchdown.</code> | <code>在一些照片中 ， 脚垫确实挤进了后台 ， 尤其是当他们在触地时侧面移动时。</code>            | <code>0.33000001311302185</code> |
+* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "pairwise_cos_sim"
+  }
+  ```
+
+#### mlqe_et_en
+
+* Dataset: [mlqe_et_en](https://huggingface.co/datasets/wmt/wmt20_mlqe_task1) at [0783ed2](https://huggingface.co/datasets/wmt/wmt20_mlqe_task1/tree/0783ed2bd75f44835df4ea664f9ccb85812c8563)
+* Size: 7,000 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: 14 tokens</li><li>mean: 31.88 tokens</li><li>max: 63 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 24.57 tokens</li><li>max: 56 tokens</li></ul> | <ul><li>min: 0.03</li><li>mean: 0.67</li><li>max: 1.0</li></ul> |
+* Samples:
+  | sentence1                                                                                                                         | sentence2                                                                                                                                      | score                           |
+  |:----------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------|
+  | <code>Gruusias vahistati president Mihhail Saakašvili pressibüroo nõunik Simon Kiladze, keda süüdistati spioneerimises.</code>    | <code>In Georgia, an adviser to the press office of President Mikhail Saakashvili, Simon Kiladze, was arrested and accused of spying.</code>   | <code>0.9466666579246521</code> |
+  | <code>Nii teadmissotsioloogia pooldajad tavaliselt Kuhni tõlgendavadki, arendades tema vaated sõnaselgeks relativismiks.</code>   | <code>This is how supporters of knowledge sociology usually interpret Kuhn by developing his views into an explicit relativism.</code>         | <code>0.9366666674613953</code> |
+  | <code>18. jaanuaril 2003 haarasid mitmeid Canberra eeslinnu võsapõlengud, milles hukkus neli ja sai vigastada 435 inimest.</code> | <code>On 18 January 2003, several of the suburbs of Canberra were seized by debt fires which killed four people and injured 435 people.</code> | <code>0.8666666150093079</code> |
+* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "pairwise_cos_sim"
+  }
+  ```
+
+#### mlqe_ne_en
+
+* Dataset: [mlqe_ne_en](https://huggingface.co/datasets/wmt/wmt20_mlqe_task1) at [0783ed2](https://huggingface.co/datasets/wmt/wmt20_mlqe_task1/tree/0783ed2bd75f44835df4ea664f9ccb85812c8563)
+* Size: 7,000 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: 17 tokens</li><li>mean: 40.67 tokens</li><li>max: 77 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 24.66 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>min: 0.01</li><li>mean: 0.39</li><li>max: 1.0</li></ul> |
+* Samples:
+  | sentence1                                                                                                   | sentence2                                                                                                  | score                            |
+  |:------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------|:---------------------------------|
+  | <code>सामान्‍य बजट प्रायः फेब्रुअरीका अंतिम कार्य दिवसमा लाईन्छ।</code>                                     | <code>A normal budget is usually awarded to the digital working day of February.</code>                    | <code>0.5600000023841858</code>  |
+  | <code>कविताका यस्ता स्वरूपमा दुई, तिन वा चार पाउसम्मका मुक्तक, हाइकु, सायरी र लोकसूक्तिहरू पर्दछन् ।</code> | <code>The book consists of two, free of her or four paulets, haiku, Sairi, and locus in such forms.</code> | <code>0.23666666448116302</code> |
+  | <code>ब्रिट्नीले यस बारेमा प्रतिक्रिया ब्यक्ता गरदै भनिन,"कुन ठूलो कुरा ���ो र?</code>                        | <code>Britney did not respond to this, saying "which is a big thing and a big thing?</code>                | <code>0.21666665375232697</code> |
+* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "pairwise_cos_sim"
+  }
+  ```
+
+#### mlqe_ro_en
+
+* Dataset: [mlqe_ro_en](https://huggingface.co/datasets/wmt/wmt20_mlqe_task1) at [0783ed2](https://huggingface.co/datasets/wmt/wmt20_mlqe_task1/tree/0783ed2bd75f44835df4ea664f9ccb85812c8563)
+* Size: 7,000 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: 12 tokens</li><li>mean: 29.44 tokens</li><li>max: 60 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 22.38 tokens</li><li>max: 65 tokens</li></ul> | <ul><li>min: 0.01</li><li>mean: 0.68</li><li>max: 1.0</li></ul> |
+* Samples:
+  | sentence1                                                                                                                                                | sentence2                                                                                                                                                                                | score                            |
+  |:---------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------|
+  | <code>Orașul va fi împărțit în patru districte, iar suburbiile în 10 mahalale.</code>                                                                    | <code>The city will be divided into four districts and suburbs into 10 mahalals.</code>                                                                                                  | <code>0.4699999988079071</code>  |
+  | <code>La scurt timp după aceasta, au devenit cunoscute debarcările germane de la Trondheim, Bergen și Stavanger, precum și luptele din Oslofjord.</code> | <code>In the light of the above, the Authority concludes that the aid granted to ADIF is compatible with the internal market pursuant to Article 61 (3) (c) of the EEA Agreement.</code> | <code>0.02666666731238365</code> |
+  | <code>Până în vara 1791, în Clubul iacobinilor au dominat reprezentanții monarhismului liberal constituțional.</code>                                    | <code>Until the summer of 1791, representatives of liberal constitutional monarchism dominated in the Jacobins Club.</code>                                                              | <code>0.8733333349227905</code>  |
+* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "pairwise_cos_sim"
+  }
+  ```
+
+#### mlqe_si_en
+
+* Dataset: [mlqe_si_en](https://huggingface.co/datasets/wmt/wmt20_mlqe_task1) at [0783ed2](https://huggingface.co/datasets/wmt/wmt20_mlqe_task1/tree/0783ed2bd75f44835df4ea664f9ccb85812c8563)
+* Size: 7,000 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: 8 tokens</li><li>mean: 18.19 tokens</li><li>max: 38 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 22.31 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>min: 0.01</li><li>mean: 0.51</li><li>max: 1.0</li></ul> |
+* Samples:
+  | sentence1                                                                                                       | sentence2                                                                                                                               | score                            |
+  |:----------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------|
+  | <code>ඇපලෝ 4 සැටර්න් V බූස්ටරයේ ප්‍රථම පර්යේෂණ පියාසැරිය විය.</code>                                            | <code>The first research flight of the Apollo 4 Saturn V Booster.</code>                                                                | <code>0.7966666221618652</code>  |
+  | <code>මෙහි අවපාතය සැලකීමේ දී, මෙහි 48%ක අවරෝහණය $ මිලියන 125කට අධික චිත්‍රපටයක් ලද තෙවන කුඩාම අවපාතය වේ.</code> | <code>In conjunction with the depression here, 48 % of obesity here is the third smallest depression in over $ 125 million film.</code> | <code>0.17666666209697723</code> |
+  | <code>එසේම "බකමූණන් මගින් මෙම රාක්ෂසියගේ රාත්‍රී හැසිරීම සංකේතවත් වන බව" පවසයි.</code>                          | <code>Also "the owl says that this monster's night behavior is symbolic".</code>                                                        | <code>0.8799999952316284</code>  |
+* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "pairwise_cos_sim"
+  }
+  ```
+
+### Evaluation Datasets
+
+#### wmt_da
+
+* Dataset: [wmt_da](https://huggingface.co/datasets/RicardoRei/wmt-da-human-evaluation) at [301de38](https://huggingface.co/datasets/RicardoRei/wmt-da-human-evaluation/tree/301de385bf05b0c00a8f4be74965e186164dd425)
+* Size: 1,285,190 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: 4 tokens</li><li>mean: 36.52 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 36.59 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.7</li><li>max: 1.0</li></ul> |
+* Samples:
+  | sentence1                                                                                                                                                                                         | sentence2                                                                                                                                                                                              | score             |
+  |:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------|
+  | <code>The note adds that should the departure from the White House be delayed, a second aircrew would be needed for the return flight due to duty-hour restrictions.</code>                       | <code>V poznámce se dodává, že pokud by se odlet z Bílého domu zpozdil, byla by pro zpáteční let kvůli omezení pracovní doby nutná druhá letecká posádka.</code>                                       | <code>0.95</code> |
+  | <code>上半年电信网络诈骗犯罪上升七成 最高检���结特点-中新网</code>                                                                                                                                                          | <code>In the first half of the year, telecommunication network fraud crimes rose by 70%. The highest inspection summary characteristics-Zhongxin.com</code>                                            | <code>0.72</code> |
+  | <code>Als zentrale Herausforderungen für den Bundesnachrichtendienst (BND) nannte Merkel den Kampf gegen die Verbreitung von Falschmeldungen im Internet und die Abwehr von Cyberattacken.</code> | <code>Merkel a cité la lutte contre la propagation de fausses nouvelles en ligne et la défense contre les cyberattaques comme des défis majeurs pour le service fédéral de renseignement (BND).</code> | <code>0.87</code> |
+* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "pairwise_cos_sim"
+  }
+  ```
+
+#### mlqe_en_de
+
+* Dataset: [mlqe_en_de](https://huggingface.co/datasets/wmt/wmt20_mlqe_task1) at [0783ed2](https://huggingface.co/datasets/wmt/wmt20_mlqe_task1/tree/0783ed2bd75f44835df4ea664f9ccb85812c8563)
+* Size: 1,000 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: 11 tokens</li><li>mean: 24.11 tokens</li><li>max: 49 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 26.66 tokens</li><li>max: 55 tokens</li></ul> | <ul><li>min: 0.03</li><li>mean: 0.81</li><li>max: 1.0</li></ul> |
+* Samples:
+  | sentence1                                                                                                                                                       | sentence2                                                                                                                                                                                                   | score                           |
+  |:----------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------|
+  | <code>Resuming her patrols, Constitution managed to recapture the American sloop Neutrality on 27 March and, a few days later, the French ship Carteret.</code> | <code>Mit der Wiederaufnahme ihrer Patrouillen gelang es der Verfassung, am 27. März die amerikanische Schleuderneutralität und wenige Tage später das französische Schiff Carteret zurückzuerobern.</code> | <code>0.9033333659172058</code> |
+  | <code>Blaine's nomination alienated many Republicans who viewed Blaine as ambitious and immoral.</code>                                                         | <code>Blaines Nominierung entfremdete viele Republikaner, die Blaine als ehrgeizig und unmoralisch betrachteten.</code>                                                                                     | <code>0.9216666221618652</code> |
+  | <code>This initiated a brief correspondence between the two which quickly descended into political rancor.</code>                                               | <code>Dies leitete eine kurze Korrespondenz zwischen den beiden ein, die schnell zu politischem Groll abstieg.</code>                                                                                       | <code>0.878333330154419</code>  |
+* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "pairwise_cos_sim"
+  }
+  ```
+
+#### mlqe_en_zh
+
+* Dataset: [mlqe_en_zh](https://huggingface.co/datasets/wmt/wmt20_mlqe_task1) at [0783ed2](https://huggingface.co/datasets/wmt/wmt20_mlqe_task1/tree/0783ed2bd75f44835df4ea664f9ccb85812c8563)
+* Size: 1,000 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: 9 tokens</li><li>mean: 23.75 tokens</li><li>max: 49 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 29.56 tokens</li><li>max: 67 tokens</li></ul> | <ul><li>min: 0.26</li><li>mean: 0.65</li><li>max: 0.9</li></ul> |
+* Samples:
+  | sentence1                                                                                                            | sentence2                                             | score                           |
+  |:---------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------|:--------------------------------|
+  | <code>Freeman briefly stayed with the king before returning to Accra via Whydah, Ahgwey and Little Popo.</code>      | <code>弗里曼在经过惠达、阿格威和小波波回到阿克拉之前与国王一起住了一会儿。</code>       | <code>0.6683333516120911</code> |
+  | <code>Fantastic Fiction "Scratches in the Sky, Ben Peek, Agog!</code>                                                | <code>奇特的虚构 "天空中的碎片 ， 本佩克 ， 阿戈 ！</code>               | <code>0.71833336353302</code>   |
+  | <code>For Hermann Keller, the running quavers and semiquavers "suffuse the setting with health and strength."</code> | <code>对赫尔曼 · 凯勒来说 ， 跑步的跳跃者和半跳跃者 "让环境充满健康和力量" 。</code> | <code>0.7066666483879089</code> |
+* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "pairwise_cos_sim"
+  }
+  ```
+
+#### mlqe_et_en
+
+* Dataset: [mlqe_et_en](https://huggingface.co/datasets/wmt/wmt20_mlqe_task1) at [0783ed2](https://huggingface.co/datasets/wmt/wmt20_mlqe_task1/tree/0783ed2bd75f44835df4ea664f9ccb85812c8563)
+* Size: 1,000 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: 12 tokens</li><li>mean: 32.4 tokens</li><li>max: 58 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 24.87 tokens</li><li>max: 47 tokens</li></ul> | <ul><li>min: 0.03</li><li>mean: 0.6</li><li>max: 0.99</li></ul> |
+* Samples:
+  | sentence1                                                                                     | sentence2                                                                                                             | score                            |
+  |:----------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------|:---------------------------------|
+  | <code>Jackson pidas seal kõne, öeldes, et James Brown on tema suurim inspiratsioon.</code>    | <code>Jackson gave a speech there saying that James Brown is his greatest inspiration.</code>                         | <code>0.9833333492279053</code>  |
+  | <code>Kaanelugu rääkis loo kolme ungarlase üleelamistest Ungari revolutsiooni päevil.</code>  | <code>The life of the Man spoke of a story of three Hungarians living in the days of the Hungarian Revolution.</code> | <code>0.28999999165534973</code> |
+  | <code>Teise maailmasõja ajal oli ta mitme Saksa juhatusele alluvate eesti väeosa ülem.</code> | <code>During World War II, he was the commander of several of the German leadership.</code>                           | <code>0.4516666829586029</code>  |
+* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "pairwise_cos_sim"
+  }
+  ```
+
+#### mlqe_ne_en
+
+* Dataset: [mlqe_ne_en](https://huggingface.co/datasets/wmt/wmt20_mlqe_task1) at [0783ed2](https://huggingface.co/datasets/wmt/wmt20_mlqe_task1/tree/0783ed2bd75f44835df4ea664f9ccb85812c8563)
+* Size: 1,000 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: 17 tokens</li><li>mean: 41.03 tokens</li><li>max: 85 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 24.77 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>min: 0.05</li><li>mean: 0.36</li><li>max: 0.92</li></ul> |
+* Samples:
+  | sentence1                                                                                 | sentence2                                                                              | score                            |
+  |:------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------|:---------------------------------|
+  | <code>१८९२ तिर भवानीदत्त पाण्डेले 'मुद्रा राक्षस'को अनुवाद गरे।</code>                    | <code>Around 1892, Bhavani Pandit translated the 'money monster'.</code>               | <code>0.8416666388511658</code>  |
+  | <code>यस बच्चाको मुखले आमाको स्तन यस बच्चाको मुखले आमाको स्तन राम्ररी च्यापेको छ ।</code> | <code>The breasts of this child's mouth are taped well with the mother's mouth.</code> | <code>0.2150000035762787</code>  |
+  | <code>बुवाको बन्दुक चोरेर हिँडेका बराललाई केआई सिंहले अब गोली ल्याउन लगाए ।...</code>     | <code>Kei Singh, who stole the boy's closet, took the bullet to bring it now..</code>  | <code>0.27000001072883606</code> |
+* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "pairwise_cos_sim"
+  }
+  ```
+
+#### mlqe_ro_en
+
+* Dataset: [mlqe_ro_en](https://huggingface.co/datasets/wmt/wmt20_mlqe_task1) at [0783ed2](https://huggingface.co/datasets/wmt/wmt20_mlqe_task1/tree/0783ed2bd75f44835df4ea664f9ccb85812c8563)
+* Size: 1,000 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: 14 tokens</li><li>mean: 30.25 tokens</li><li>max: 59 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 22.7 tokens</li><li>max: 55 tokens</li></ul> | <ul><li>min: 0.01</li><li>mean: 0.68</li><li>max: 1.0</li></ul> |
+* Samples:
+  | sentence1                                                                                                                                           | sentence2                                                                                                                           | score                             |
+  |:----------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------|
+  | <code>Cornwallis se afla înconjurat pe uscat de forțe armate net superioare și retragerea pe mare era îndoielnică din cauza flotei franceze.</code> | <code>Cornwallis was surrounded by shore by higher armed forces and the sea withdrawal was doubtful due to the French fleet.</code> | <code>0.8199999928474426</code>   |
+  | <code>thumbrightuprightDansatori [[cretani de muzică tradițională.</code>                                                                           | <code>Number of employees employed in the production of the like product in the Union.</code>                                       | <code>0.009999999776482582</code> |
+  | <code>Potrivit documentelor vremii și tradiției orale, aceasta a fost cea mai grea perioadă din istoria orașului.</code>                            | <code>According to the documents of the oral weather and tradition, this was the hardest period in the city's history.</code>       | <code>0.5383332967758179</code>   |
+* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "pairwise_cos_sim"
+  }
+  ```
+
+#### mlqe_si_en
+
+* Dataset: [mlqe_si_en](https://huggingface.co/datasets/wmt/wmt20_mlqe_task1) at [0783ed2](https://huggingface.co/datasets/wmt/wmt20_mlqe_task1/tree/0783ed2bd75f44835df4ea664f9ccb85812c8563)
+* Size: 1,000 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: 8 tokens</li><li>mean: 18.12 tokens</li><li>max: 36 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 22.18 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>min: 0.03</li><li>mean: 0.51</li><li>max: 0.99</li></ul> |
+* Samples:
+  | sentence1                                                                                                                               | sentence2                                                                                     | score                           |
+  |:----------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------|:--------------------------------|
+  | <code>එයට ශි්‍ර ලංකාවේ සාමය ඇති කිරිමටත් නැති කිරිමටත් පුළුවන්.</code>                                                                  | <code>It can also cause peace in Sri Lanka.</code>                                            | <code>0.3199999928474426</code> |
+  | <code>ඔහු මනෝ විද්‍යාව, සමාජ විද්‍යාව, ඉතිහාසය හා සන්නිවේදනය යන විෂය ක්ෂේත්‍රයන් පිලිබදවද අධ්‍යයනයන් සිදු කිරීමට උත්සාහ කරන ලදි.</code> | <code>He attempted to do subjects in psychology, sociology, history and communication.</code> | <code>0.5366666913032532</code> |
+  | <code>එහෙත් කිසිදු මිනිසෙක්‌ හෝ ගැහැනියෙක්‌ එලිමහනක නොවූහ.</code>                                                                       | <code>But no man or woman was eliminated.</code>                                              | <code>0.2783333361148834</code> |
+* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "pairwise_cos_sim"
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 64
+- `per_device_eval_batch_size`: 64
+- `num_train_epochs`: 2
+- `warmup_ratio`: 0.1
+
+#### 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`: 64
+- `per_device_eval_batch_size`: 64
+- `per_gpu_train_batch_size`: None
+- `per_gpu_eval_batch_size`: None
+- `gradient_accumulation_steps`: 1
+- `eval_accumulation_steps`: None
+- `torch_empty_cache_steps`: None
+- `learning_rate`: 5e-05
+- `weight_decay`: 0.0
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `max_grad_norm`: 1.0
+- `num_train_epochs`: 2
+- `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`: False
+- `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`: None
+- `hub_always_push`: False
+- `gradient_checkpointing`: False
+- `gradient_checkpointing_kwargs`: None
+- `include_inputs_for_metrics`: False
+- `include_for_metrics`: []
+- `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
+- `use_liger_kernel`: False
+- `eval_use_gather_object`: False
+- `average_tokens_across_devices`: False
+- `prompts`: None
+- `batch_sampler`: batch_sampler
+- `multi_dataset_batch_sampler`: proportional
+
+</details>
+
+### Training Logs
+| Epoch | Step  | Training Loss | wmt da loss | mlqe en de loss | mlqe en zh loss | mlqe et en loss | mlqe ne en loss | mlqe ro en loss | mlqe si en loss | sts-eval_spearman_cosine | sts-test_spearman_cosine |
+|:-----:|:-----:|:-------------:|:-----------:|:---------------:|:---------------:|:---------------:|:---------------:|:---------------:|:---------------:|:------------------------:|:------------------------:|
+| 0.4   | 6690  | 7.8421        | 7.5547      | 7.5619          | 7.5555          | 7.5327          | 7.5354          | 7.5109          | 7.5564          | 0.1989                   | -                        |
+| 0.8   | 13380 | 7.552         | 7.5420      | 7.5757          | 7.5739          | 7.5185          | 7.5126          | 7.4994          | 7.5511          | 0.2336                   | -                        |
+| 1.2   | 20070 | 7.5216        | 7.5465      | 7.6072          | 7.5942          | 7.5217          | 7.5141          | 7.4871          | 7.5471          | 0.2694                   | -                        |
+| 1.6   | 26760 | 7.5024        | 7.5329      | 7.6123          | 7.5814          | 7.5230          | 7.5141          | 7.4679          | 7.5379          | 0.2866                   | -                        |
+| 2.0   | 33450 | 7.495         | 7.5252      | 7.6106          | 7.5756          | 7.5201          | 7.5128          | 7.4725          | 7.5417          | 0.2814                   | 0.2807                   |
+
+
+### Framework Versions
+- Python: 3.11.10
+- Sentence Transformers: 3.3.1
+- Transformers: 4.47.1
+- PyTorch: 2.3.1+cu121
+- Accelerate: 1.2.1
+- Datasets: 3.2.0
+- Tokenizers: 0.21.0
+
+## 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",
+}
+```
+
+#### CoSENTLoss
+```bibtex
+@online{kexuefm-8847,
+    title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT},
+    author={Su Jianlin},
+    year={2022},
+    month={Jan},
+    url={https://kexue.fm/archives/8847},
+}
+```
+
+<!--
+## 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.*
+-->

config.json

@@ -0,0 +1,26 @@
+{
+  "_name_or_path": "sentence-transformers/distiluse-base-multilingual-cased-v2",
+  "activation": "gelu",
+  "architectures": [
+    "DistilBertModel"
+  ],
+  "attention_dropout": 0.1,
+  "dim": 768,
+  "dropout": 0.1,
+  "hidden_dim": 3072,
+  "initializer_range": 0.02,
+  "max_position_embeddings": 512,
+  "model_type": "distilbert",
+  "n_heads": 12,
+  "n_layers": 6,
+  "output_hidden_states": true,
+  "output_past": true,
+  "pad_token_id": 0,
+  "qa_dropout": 0.1,
+  "seq_classif_dropout": 0.2,
+  "sinusoidal_pos_embds": false,
+  "tie_weights_": true,
+  "torch_dtype": "float32",
+  "transformers_version": "4.47.1",
+  "vocab_size": 119547
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.3.1",
+    "transformers": "4.47.1",
+    "pytorch": "2.3.1+cu121"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:dbbc5f2d5511cb456059e2355c006214e670f7b2d9b3b879412e673e4aeab832
+size 538947416

modules.json

@@ -0,0 +1,20 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  },
+  {
+    "idx": 2,
+    "name": "2",
+    "path": "2_Dense",
+    "type": "sentence_transformers.models.Dense"
+  }
+]

sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+  "max_seq_length": 128,
+  "do_lower_case": false
+}

special_tokens_map.json

@@ -0,0 +1,37 @@
+{
+  "cls_token": {
+    "content": "[CLS]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "[MASK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "[PAD]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "[SEP]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

@@ -0,0 +1,60 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "101": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "102": {
+      "content": "[SEP]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "103": {
+      "content": "[MASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": false,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": false,
+  "extra_special_tokens": {},
+  "full_tokenizer_file": null,
+  "mask_token": "[MASK]",
+  "max_len": 512,
+  "model_max_length": 128,
+  "never_split": null,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "DistilBertTokenizer",
+  "unk_token": "[UNK]"
+}