.

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "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
+}

README.md

@@ -0,0 +1,703 @@
+---
+language:
+- en
+library_name: sentence-transformers
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:314315
+- loss:AdaptiveLayerLoss
+- loss:MultipleNegativesRankingLoss
+base_model: microsoft/deberta-v3-small
+datasets:
+- stanfordnlp/snli
+- sentence-transformers/stsb
+metrics:
+- pearson_cosine
+- spearman_cosine
+- pearson_manhattan
+- spearman_manhattan
+- pearson_euclidean
+- spearman_euclidean
+- pearson_dot
+- spearman_dot
+- pearson_max
+- spearman_max
+- cosine_accuracy
+- cosine_accuracy_threshold
+- cosine_f1
+- cosine_f1_threshold
+- cosine_precision
+- cosine_recall
+- cosine_ap
+- dot_accuracy
+- dot_accuracy_threshold
+- dot_f1
+- dot_f1_threshold
+- dot_precision
+- dot_recall
+- dot_ap
+- manhattan_accuracy
+- manhattan_accuracy_threshold
+- manhattan_f1
+- manhattan_f1_threshold
+- manhattan_precision
+- manhattan_recall
+- manhattan_ap
+- euclidean_accuracy
+- euclidean_accuracy_threshold
+- euclidean_f1
+- euclidean_f1_threshold
+- euclidean_precision
+- euclidean_recall
+- euclidean_ap
+- max_accuracy
+- max_accuracy_threshold
+- max_f1
+- max_f1_threshold
+- max_precision
+- max_recall
+- max_ap
+widget:
+- source_sentence: The pitcher is pitching the ball in a game of baseball.
+  sentences:
+  - the lady digs into the ground
+  - A group of people are sitting at tables.
+  - The pitcher throws the ball.
+- source_sentence: People are conversing at a dining table under a canopy.
+  sentences:
+  - A canine is using his legs.
+  - The people are creative.
+  - People at a party are seated for dinner on the lawn.
+- source_sentence: Two teenage girls conversing next to lockers.
+  sentences:
+  - Girls talking about their problems next to lockers.
+  - A group of people play in the ocean.
+  - The man is testing the bike.
+- source_sentence: A young boy in a hoodie climbs a red slide sitting on a red and
+    green checkered background.
+  sentences:
+  - People are buying food from a street vendor.
+  - A boy is playing.
+  - A dog outside digging.
+- source_sentence: A professional swimmer spits water out after surfacing while grabbing
+    the hand of someone helping him back to land.
+  sentences:
+  - A group of people wait in a line.
+  - A tourist has his picture taken on Easter Island.
+  - The swimmer almost drowned after being sucked under a fast current.
+pipeline_tag: sentence-similarity
+model-index:
+- name: SentenceTransformer based on microsoft/deberta-v3-small
+  results:
+  - task:
+      type: semantic-similarity
+      name: Semantic Similarity
+    dataset:
+      name: Unknown
+      type: unknown
+    metrics:
+    - type: pearson_cosine
+      value: 0.7641416788909702
+      name: Pearson Cosine
+    - type: spearman_cosine
+      value: 0.763668633314844
+      name: Spearman Cosine
+    - type: pearson_manhattan
+      value: 0.7808845626705342
+      name: Pearson Manhattan
+    - type: spearman_manhattan
+      value: 0.783960481366303
+      name: Spearman Manhattan
+    - type: pearson_euclidean
+      value: 0.7714319160162553
+      name: Pearson Euclidean
+    - type: spearman_euclidean
+      value: 0.7750607015673249
+      name: Spearman Euclidean
+    - type: pearson_dot
+      value: 0.587659176024498
+      name: Pearson Dot
+    - type: spearman_dot
+      value: 0.6010467058509925
+      name: Spearman Dot
+    - type: pearson_max
+      value: 0.7808845626705342
+      name: Pearson Max
+    - type: spearman_max
+      value: 0.783960481366303
+      name: Spearman Max
+  - task:
+      type: binary-classification
+      name: Binary Classification
+    dataset:
+      name: Unknown
+      type: unknown
+    metrics:
+    - type: cosine_accuracy
+      value: 0.6773826673743271
+      name: Cosine Accuracy
+    - type: cosine_accuracy_threshold
+      value: 0.5830236673355103
+      name: Cosine Accuracy Threshold
+    - type: cosine_f1
+      value: 0.7209834880077135
+      name: Cosine F1
+    - type: cosine_f1_threshold
+      value: 0.5085207223892212
+      name: Cosine F1 Threshold
+    - type: cosine_precision
+      value: 0.6137273007079102
+      name: Cosine Precision
+    - type: cosine_recall
+      value: 0.873667299547247
+      name: Cosine Recall
+    - type: cosine_ap
+      value: 0.7219177301725319
+      name: Cosine Ap
+    - type: dot_accuracy
+      value: 0.6389415421942528
+      name: Dot Accuracy
+    - type: dot_accuracy_threshold
+      value: 45.1016845703125
+      name: Dot Accuracy Threshold
+    - type: dot_f1
+      value: 0.7090406632451638
+      name: Dot F1
+    - type: dot_f1_threshold
+      value: 32.459449768066406
+      name: Dot F1 Threshold
+    - type: dot_precision
+      value: 0.5775450202131569
+      name: Dot Precision
+    - type: dot_recall
+      value: 0.9180663064115671
+      name: Dot Recall
+    - type: dot_ap
+      value: 0.6795197111227502
+      name: Dot Ap
+    - type: manhattan_accuracy
+      value: 0.6625217984684206
+      name: Manhattan Accuracy
+    - type: manhattan_accuracy_threshold
+      value: 158.29489135742188
+      name: Manhattan Accuracy Threshold
+    - type: manhattan_f1
+      value: 0.7041269465332466
+      name: Manhattan F1
+    - type: manhattan_f1_threshold
+      value: 178.5047607421875
+      name: Manhattan F1 Threshold
+    - type: manhattan_precision
+      value: 0.5921131248755228
+      name: Manhattan Precision
+    - type: manhattan_recall
+      value: 0.8684095224185775
+      name: Manhattan Recall
+    - type: manhattan_ap
+      value: 0.7054112942825768
+      name: Manhattan Ap
+    - type: euclidean_accuracy
+      value: 0.6578967321252559
+      name: Euclidean Accuracy
+    - type: euclidean_accuracy_threshold
+      value: 7.951424598693848
+      name: Euclidean Accuracy Threshold
+    - type: euclidean_f1
+      value: 0.7015471831817645
+      name: Euclidean F1
+    - type: euclidean_f1_threshold
+      value: 9.045232772827148
+      name: Euclidean F1 Threshold
+    - type: euclidean_precision
+      value: 0.5888767720828789
+      name: Euclidean Precision
+    - type: euclidean_recall
+      value: 0.8675332262304659
+      name: Euclidean Recall
+    - type: euclidean_ap
+      value: 0.7024193897121154
+      name: Euclidean Ap
+    - type: max_accuracy
+      value: 0.6773826673743271
+      name: Max Accuracy
+    - type: max_accuracy_threshold
+      value: 158.29489135742188
+      name: Max Accuracy Threshold
+    - type: max_f1
+      value: 0.7209834880077135
+      name: Max F1
+    - type: max_f1_threshold
+      value: 178.5047607421875
+      name: Max F1 Threshold
+    - type: max_precision
+      value: 0.6137273007079102
+      name: Max Precision
+    - type: max_recall
+      value: 0.9180663064115671
+      name: Max Recall
+    - type: max_ap
+      value: 0.7219177301725319
+      name: Max Ap
+---
+
+# SentenceTransformer based on microsoft/deberta-v3-small
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) on the [stanfordnlp/snli](https://huggingface.co/datasets/stanfordnlp/snli) dataset. It maps sentences & paragraphs to a 768-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:** [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) <!-- at revision a36c739020e01763fe789b4b85e2df55d6180012 -->
+- **Maximum Sequence Length:** 512 tokens
+- **Output Dimensionality:** 768 tokens
+- **Similarity Function:** Cosine Similarity
+- **Training Dataset:**
+    - [stanfordnlp/snli](https://huggingface.co/datasets/stanfordnlp/snli)
+- **Language:** en
+<!-- - **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': 512, 'do_lower_case': False}) with Transformer model: DebertaV2Model 
+  (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})
+)
+```
+
+## 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("bobox/DeBERTaV3-small-SentenceTransformer-AdaptiveLayerAll")
+# Run inference
+sentences = [
+    'A professional swimmer spits water out after surfacing while grabbing the hand of someone helping him back to land.',
+    'The swimmer almost drowned after being sucked under a fast current.',
+    'A group of people wait in a line.',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 768]
+
+# 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
+
+* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| pearson_cosine      | 0.7641     |
+| **spearman_cosine** | **0.7637** |
+| pearson_manhattan   | 0.7809     |
+| spearman_manhattan  | 0.784      |
+| pearson_euclidean   | 0.7714     |
+| spearman_euclidean  | 0.7751     |
+| pearson_dot         | 0.5877     |
+| spearman_dot        | 0.601      |
+| pearson_max         | 0.7809     |
+| spearman_max        | 0.784      |
+
+#### Binary Classification
+
+* Evaluated with [<code>BinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator)
+
+| Metric                       | Value      |
+|:-----------------------------|:-----------|
+| cosine_accuracy              | 0.6774     |
+| cosine_accuracy_threshold    | 0.583      |
+| cosine_f1                    | 0.721      |
+| cosine_f1_threshold          | 0.5085     |
+| cosine_precision             | 0.6137     |
+| cosine_recall                | 0.8737     |
+| cosine_ap                    | 0.7219     |
+| dot_accuracy                 | 0.6389     |
+| dot_accuracy_threshold       | 45.1017    |
+| dot_f1                       | 0.709      |
+| dot_f1_threshold             | 32.4594    |
+| dot_precision                | 0.5775     |
+| dot_recall                   | 0.9181     |
+| dot_ap                       | 0.6795     |
+| manhattan_accuracy           | 0.6625     |
+| manhattan_accuracy_threshold | 158.2949   |
+| manhattan_f1                 | 0.7041     |
+| manhattan_f1_threshold       | 178.5048   |
+| manhattan_precision          | 0.5921     |
+| manhattan_recall             | 0.8684     |
+| manhattan_ap                 | 0.7054     |
+| euclidean_accuracy           | 0.6579     |
+| euclidean_accuracy_threshold | 7.9514     |
+| euclidean_f1                 | 0.7015     |
+| euclidean_f1_threshold       | 9.0452     |
+| euclidean_precision          | 0.5889     |
+| euclidean_recall             | 0.8675     |
+| euclidean_ap                 | 0.7024     |
+| max_accuracy                 | 0.6774     |
+| max_accuracy_threshold       | 158.2949   |
+| max_f1                       | 0.721      |
+| max_f1_threshold             | 178.5048   |
+| max_precision                | 0.6137     |
+| max_recall                   | 0.9181     |
+| **max_ap**                   | **0.7219** |
+
+<!--
+## 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
+
+#### stanfordnlp/snli
+
+* Dataset: [stanfordnlp/snli](https://huggingface.co/datasets/stanfordnlp/snli) at [cdb5c3d](https://huggingface.co/datasets/stanfordnlp/snli/tree/cdb5c3d5eed6ead6e5a341c8e56e669bb666725b)
+* Size: 314,315 training samples
+* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | sentence1                                                                         | sentence2                                                                        | label                        |
+  |:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:-----------------------------|
+  | type    | string                                                                            | string                                                                           | int                          |
+  | details | <ul><li>min: 5 tokens</li><li>mean: 16.62 tokens</li><li>max: 62 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 9.46 tokens</li><li>max: 29 tokens</li></ul> | <ul><li>0: 100.00%</li></ul> |
+* Samples:
+  | sentence1                                                                  | sentence2                                        | label          |
+  |:---------------------------------------------------------------------------|:-------------------------------------------------|:---------------|
+  | <code>A person on a horse jumps over a broken down airplane.</code>        | <code>A person is outdoors, on a horse.</code>   | <code>0</code> |
+  | <code>Children smiling and waving at camera</code>                         | <code>There are children present</code>          | <code>0</code> |
+  | <code>A boy is jumping on skateboard in the middle of a red bridge.</code> | <code>The boy does a skateboarding trick.</code> | <code>0</code> |
+* Loss: [<code>AdaptiveLayerLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters:
+  ```json
+  {
+      "loss": "MultipleNegativesRankingLoss",
+      "n_layers_per_step": -1,
+      "last_layer_weight": 1,
+      "prior_layers_weight": 1,
+      "kl_div_weight": 1.2,
+      "kl_temperature": 1.2
+  }
+  ```
+
+### Evaluation Dataset
+
+#### sentence-transformers/stsb
+
+* Dataset: [sentence-transformers/stsb](https://huggingface.co/datasets/sentence-transformers/stsb) at [ab7a5ac](https://huggingface.co/datasets/sentence-transformers/stsb/tree/ab7a5ac0e35aa22088bdcf23e7fd99b220e53308)
+* 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: 14.77 tokens</li><li>max: 45 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 14.74 tokens</li><li>max: 49 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>A man with a hard hat is dancing.</code>    | <code>A man wearing a hard hat is dancing.</code>     | <code>1.0</code>  |
+  | <code>A young child is riding a horse.</code>     | <code>A child is riding a horse.</code>               | <code>0.95</code> |
+  | <code>A man is feeding a mouse to a snake.</code> | <code>The man is feeding a mouse to the snake.</code> | <code>1.0</code>  |
+* Loss: [<code>AdaptiveLayerLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters:
+  ```json
+  {
+      "loss": "MultipleNegativesRankingLoss",
+      "n_layers_per_step": -1,
+      "last_layer_weight": 1,
+      "prior_layers_weight": 1,
+      "kl_div_weight": 1.2,
+      "kl_temperature": 1.2
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 32
+- `per_device_eval_batch_size`: 16
+- `learning_rate`: 5e-06
+- `weight_decay`: 1e-07
+- `warmup_ratio`: 0.33
+- `save_safetensors`: False
+- `fp16`: True
+- `push_to_hub`: True
+- `hub_model_id`: bobox/DeBERTaV3-small-SentenceTransformer-AdaptiveLayerAlln
+- `hub_strategy`: checkpoint
+- `batch_sampler`: no_duplicates
+
+#### 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`: 32
+- `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`: 1e-07
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `max_grad_norm`: 1.0
+- `num_train_epochs`: 3
+- `max_steps`: -1
+- `lr_scheduler_type`: linear
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.33
+- `warmup_steps`: 0
+- `log_level`: passive
+- `log_level_replica`: warning
+- `log_on_each_node`: True
+- `logging_nan_inf_filter`: True
+- `save_safetensors`: False
+- `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`: True
+- `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`: True
+- `resume_from_checkpoint`: None
+- `hub_model_id`: bobox/DeBERTaV3-small-SentenceTransformer-AdaptiveLayerAlln
+- `hub_strategy`: checkpoint
+- `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
+- `batch_sampler`: no_duplicates
+- `multi_dataset_batch_sampler`: proportional
+
+</details>
+
+### Training Logs
+| Epoch  | Step  | Training Loss | loss   | max_ap | spearman_cosine |
+|:------:|:-----:|:-------------:|:------:|:------:|:---------------:|
+| None   | 0     | -             | 5.4171 | -      | 0.4276          |
+| 0.1501 | 1474  | 4.9879        | -      | -      | -               |
+| 0.3000 | 2947  | -             | 2.6463 | 0.6840 | -               |
+| 0.3001 | 2948  | 3.2669        | -      | -      | -               |
+| 0.4502 | 4422  | 2.6363        | -      | -      | -               |
+| 0.6000 | 5894  | -             | 1.8436 | 0.7014 | -               |
+| 0.6002 | 5896  | 2.192         | -      | -      | -               |
+| 0.7503 | 7370  | 0.8208        | -      | -      | -               |
+| 0.9000 | 8841  | -             | 1.5551 | 0.7065 | -               |
+| 0.9003 | 8844  | 0.6161        | -      | -      | -               |
+| 1.0504 | 10318 | 1.0301        | -      | -      | -               |
+| 1.2000 | 11788 | -             | 1.1883 | 0.7131 | -               |
+| 1.2004 | 11792 | 1.8209        | -      | -      | -               |
+| 1.3505 | 13266 | 1.6887        | -      | -      | -               |
+| 1.5001 | 14735 | -             | 1.1067 | 0.7119 | -               |
+| 1.5006 | 14740 | 1.6114        | -      | -      | -               |
+| 1.6506 | 16214 | 1.0691        | -      | -      | -               |
+| 1.8001 | 17682 | -             | 1.0872 | 0.7183 | -               |
+| 1.8007 | 17688 | 0.3982        | -      | -      | -               |
+| 1.9507 | 19162 | 0.3659        | -      | -      | -               |
+| 2.1001 | 20629 | -             | 0.9642 | 0.7221 | -               |
+| 2.1008 | 20636 | 1.1702        | -      | -      | -               |
+| 2.2508 | 22110 | 1.4984        | -      | -      | -               |
+| 2.4001 | 23576 | -             | 0.9437 | 0.7200 | -               |
+| 2.4009 | 23584 | 1.4609        | -      | -      | -               |
+| 2.5510 | 25058 | 1.4477        | -      | -      | -               |
+| 2.7001 | 26523 | -             | 0.9428 | 0.7216 | -               |
+| 2.7010 | 26532 | 0.5802        | -      | -      | -               |
+| 2.8511 | 28006 | 0.3297        | -      | -      | -               |
+| 3.0    | 29469 | -             | 0.9532 | 0.7219 | -               |
+| None   | 0     | -             | 2.4079 | 0.7219 | 0.7637          |
+
+
+### Framework Versions
+- Python: 3.10.13
+- Sentence Transformers: 3.0.1
+- Transformers: 4.41.2
+- PyTorch: 2.1.2
+- Accelerate: 0.30.1
+- Datasets: 2.19.2
+- 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",
+}
+```
+
+#### AdaptiveLayerLoss
+```bibtex
+@misc{li20242d,
+    title={2D Matryoshka Sentence Embeddings}, 
+    author={Xianming Li and Zongxi Li and Jing Li and Haoran Xie and Qing Li},
+    year={2024},
+    eprint={2402.14776},
+    archivePrefix={arXiv},
+    primaryClass={cs.CL}
+}
+```
+
+#### MultipleNegativesRankingLoss
+```bibtex
+@misc{henderson2017efficient,
+    title={Efficient Natural Language Response Suggestion for Smart Reply}, 
+    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
+    year={2017},
+    eprint={1705.00652},
+    archivePrefix={arXiv},
+    primaryClass={cs.CL}
+}
+```
+
+<!--
+## 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.*
+-->

added_tokens.json

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config.json

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+  "architectures": [
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+  "model_type": "deberta-v2",
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+  "position_buckets": 256,
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+}

config_sentence_transformers.json

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+{
+  "__version__": {
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+    "transformers": "4.41.2",
+    "pytorch": "2.1.2"
+  },
+  "prompts": {},
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+  "similarity_fn_name": null
+}

modules.json

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+    "type": "sentence_transformers.models.Transformer"
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+  {
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+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  }
+]

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sentence_bert_config.json

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+  "do_lower_case": false
+}

special_tokens_map.json

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+  "unk_token": "[UNK]",
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