all layer trained for every step.

n_layers_per_step = -1, last_layer_weight = 1 * model_layers,, prior_layers_weight= 0.85, kl_div_weight = 2, kl_temperature= 10, lr = 1e-6. batch = 42, schedule = cosine

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 +1,627 @@
+---
+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
+metrics:
+- 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: binary-classification
+      name: Binary Classification
+    dataset:
+      name: Unknown
+      type: unknown
+    metrics:
+    - type: cosine_accuracy
+      value: 0.6578209113655319
+      name: Cosine Accuracy
+    - type: cosine_accuracy_threshold
+      value: 0.7228835821151733
+      name: Cosine Accuracy Threshold
+    - type: cosine_f1
+      value: 0.7058138858173776
+      name: Cosine F1
+    - type: cosine_f1_threshold
+      value: 0.6018929481506348
+      name: Cosine F1 Threshold
+    - type: cosine_precision
+      value: 0.586687306501548
+      name: Cosine Precision
+    - type: cosine_recall
+      value: 0.8856433474514386
+      name: Cosine Recall
+    - type: cosine_ap
+      value: 0.6972177912771047
+      name: Cosine Ap
+    - type: dot_accuracy
+      value: 0.6157403897187049
+      name: Dot Accuracy
+    - type: dot_accuracy_threshold
+      value: 240.6935577392578
+      name: Dot Accuracy Threshold
+    - type: dot_f1
+      value: 0.6994949494949494
+      name: Dot F1
+    - type: dot_f1_threshold
+      value: 180.59024047851562
+      name: Dot F1 Threshold
+    - type: dot_precision
+      value: 0.5603834989884774
+      name: Dot Precision
+    - type: dot_recall
+      value: 0.9304805024098145
+      name: Dot Recall
+    - type: dot_ap
+      value: 0.6228322985998769
+      name: Dot Ap
+    - type: manhattan_accuracy
+      value: 0.6658579118962772
+      name: Manhattan Accuracy
+    - type: manhattan_accuracy_threshold
+      value: 281.63262939453125
+      name: Manhattan Accuracy Threshold
+    - type: manhattan_f1
+      value: 0.7096774193548386
+      name: Manhattan F1
+    - type: manhattan_f1_threshold
+      value: 315.9024658203125
+      name: Manhattan F1 Threshold
+    - type: manhattan_precision
+      value: 0.6168446026097272
+      name: Manhattan Precision
+    - type: manhattan_recall
+      value: 0.8354023659997079
+      name: Manhattan Recall
+    - type: manhattan_ap
+      value: 0.7109579985461502
+      name: Manhattan Ap
+    - type: euclidean_accuracy
+      value: 0.6626734399878687
+      name: Euclidean Accuracy
+    - type: euclidean_accuracy_threshold
+      value: 14.194840431213379
+      name: Euclidean Accuracy Threshold
+    - type: euclidean_f1
+      value: 0.7064288581751448
+      name: Euclidean F1
+    - type: euclidean_f1_threshold
+      value: 17.004133224487305
+      name: Euclidean F1 Threshold
+    - type: euclidean_precision
+      value: 0.581586402266289
+      name: Euclidean Precision
+    - type: euclidean_recall
+      value: 0.8995180370965387
+      name: Euclidean Recall
+    - type: euclidean_ap
+      value: 0.7094433163219231
+      name: Euclidean Ap
+    - type: max_accuracy
+      value: 0.6658579118962772
+      name: Max Accuracy
+    - type: max_accuracy_threshold
+      value: 281.63262939453125
+      name: Max Accuracy Threshold
+    - type: max_f1
+      value: 0.7096774193548386
+      name: Max F1
+    - type: max_f1_threshold
+      value: 315.9024658203125
+      name: Max F1 Threshold
+    - type: max_precision
+      value: 0.6168446026097272
+      name: Max Precision
+    - type: max_recall
+      value: 0.9304805024098145
+      name: Max Recall
+    - type: max_ap
+      value: 0.7109579985461502
+      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-SenTra-AdaptiveLayerAllNorm")
+# 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
+
+#### 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              | 0.6578    |
+| cosine_accuracy_threshold    | 0.7229    |
+| cosine_f1                    | 0.7058    |
+| cosine_f1_threshold          | 0.6019    |
+| cosine_precision             | 0.5867    |
+| cosine_recall                | 0.8856    |
+| cosine_ap                    | 0.6972    |
+| dot_accuracy                 | 0.6157    |
+| dot_accuracy_threshold       | 240.6936  |
+| dot_f1                       | 0.6995    |
+| dot_f1_threshold             | 180.5902  |
+| dot_precision                | 0.5604    |
+| dot_recall                   | 0.9305    |
+| dot_ap                       | 0.6228    |
+| manhattan_accuracy           | 0.6659    |
+| manhattan_accuracy_threshold | 281.6326  |
+| manhattan_f1                 | 0.7097    |
+| manhattan_f1_threshold       | 315.9025  |
+| manhattan_precision          | 0.6168    |
+| manhattan_recall             | 0.8354    |
+| manhattan_ap                 | 0.711     |
+| euclidean_accuracy           | 0.6627    |
+| euclidean_accuracy_threshold | 14.1948   |
+| euclidean_f1                 | 0.7064    |
+| euclidean_f1_threshold       | 17.0041   |
+| euclidean_precision          | 0.5816    |
+| euclidean_recall             | 0.8995    |
+| euclidean_ap                 | 0.7094    |
+| max_accuracy                 | 0.6659    |
+| max_accuracy_threshold       | 281.6326  |
+| max_f1                       | 0.7097    |
+| max_f1_threshold             | 315.9025  |
+| max_precision                | 0.6168    |
+| max_recall                   | 0.9305    |
+| **max_ap**                   | **0.711** |
+
+<!--
+## 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": 6,
+      "prior_layers_weight": 0.85,
+      "kl_div_weight": 2,
+      "kl_temperature": 10
+  }
+  ```
+
+### Evaluation Dataset
+
+#### stanfordnlp/snli
+
+* Dataset: [stanfordnlp/snli](https://huggingface.co/datasets/stanfordnlp/snli) at [cdb5c3d](https://huggingface.co/datasets/stanfordnlp/snli/tree/cdb5c3d5eed6ead6e5a341c8e56e669bb666725b)
+* Size: 13,189 evaluation samples
+* Columns: <code>premise</code>, <code>hypothesis</code>, and <code>label</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | premise                                                                           | hypothesis                                                                        | label                                           |
+  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:------------------------------------------------|
+  | type    | string                                                                            | string                                                                            | int                                             |
+  | details | <ul><li>min: 6 tokens</li><li>mean: 17.28 tokens</li><li>max: 59 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 10.53 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>0: ~48.70%</li><li>1: ~51.30%</li></ul> |
+* Samples:
+  | premise                                                                                                 | hypothesis                                         | label          |
+  |:--------------------------------------------------------------------------------------------------------|:---------------------------------------------------|:---------------|
+  | <code>This church choir sings to the masses as they sing joyous songs from the book at a church.</code> | <code>The church has cracks in the ceiling.</code> | <code>0</code> |
+  | <code>This church choir sings to the masses as they sing joyous songs from the book at a church.</code> | <code>The church is filled with song.</code>       | <code>1</code> |
+  | <code>A woman with a green headscarf, blue shirt and a very big grin.</code>                            | <code>The woman is young.</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": 6,
+      "prior_layers_weight": 0.85,
+      "kl_div_weight": 2,
+      "kl_temperature": 10
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 42
+- `per_device_eval_batch_size`: 32
+- `learning_rate`: 1e-06
+- `weight_decay`: 1e-08
+- `num_train_epochs`: 1
+- `lr_scheduler_type`: cosine
+- `warmup_ratio`: 0.2
+- `save_safetensors`: False
+- `fp16`: True
+- `hub_model_id`: bobox/DeBERTaV3-small-SenTra-AdaptiveLayerAllNorm-tmp
+- `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`: 42
+- `per_device_eval_batch_size`: 32
+- `per_gpu_train_batch_size`: None
+- `per_gpu_eval_batch_size`: None
+- `gradient_accumulation_steps`: 1
+- `eval_accumulation_steps`: None
+- `learning_rate`: 1e-06
+- `weight_decay`: 1e-08
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `max_grad_norm`: 1.0
+- `num_train_epochs`: 1
+- `max_steps`: -1
+- `lr_scheduler_type`: cosine
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.2
+- `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`: False
+- `resume_from_checkpoint`: None
+- `hub_model_id`: bobox/DeBERTaV3-small-SenTra-AdaptiveLayerAllNorm-tmp
+- `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 |
+|:------:|:----:|:-------------:|:-------:|:------:|
+| 0.0501 | 375  | 23.8735       | 21.0352 | 0.6131 |
+| 0.1002 | 750  | 22.4091       | 19.6992 | 0.6353 |
+| 0.1503 | 1125 | 19.4663       | 16.2104 | 0.6580 |
+| 0.2004 | 1500 | 15.348        | 13.2038 | 0.6732 |
+| 0.2505 | 1875 | 12.5377       | 11.6357 | 0.6815 |
+| 0.3006 | 2250 | 11.4576       | 10.7570 | 0.6862 |
+| 0.3507 | 2625 | 10.7446       | 10.1819 | 0.6891 |
+| 0.4009 | 3000 | 10.2323       | 9.7470  | 0.6904 |
+| 0.4510 | 3375 | 9.9825        | 9.4256  | 0.6914 |
+| 0.5011 | 3750 | 9.6954        | 9.2200  | 0.6923 |
+| 0.5512 | 4125 | 9.6359        | 9.0367  | 0.6923 |
+| 0.6013 | 4500 | 8.3103        | 7.8258  | 0.7026 |
+| 0.6514 | 4875 | 4.4845        | 7.4044  | 0.7073 |
+| 0.7015 | 5250 | 3.8303        | 7.2647  | 0.7092 |
+| 0.7516 | 5625 | 3.5617        | 7.2020  | 0.7098 |
+| 0.8017 | 6000 | 3.4088        | 7.1684  | 0.7103 |
+| 0.8518 | 6375 | 3.347         | 7.1531  | 0.7108 |
+| 0.9019 | 6750 | 3.2064        | 7.1451  | 0.7109 |
+| 0.9520 | 7125 | 3.3096        | 7.1427  | 0.7110 |
+
+
+### 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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+{
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+  "architectures": [
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+  "transformers_version": "4.41.2",
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+}

config_sentence_transformers.json

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+{
+  "__version__": {
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+    "pytorch": "2.1.2"
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+  "similarity_fn_name": null
+}

modules.json

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+    "name": "0",
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+    "type": "sentence_transformers.models.Transformer"
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+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  }
+]

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sentence_bert_config.json

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+{
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+  "do_lower_case": false
+}

special_tokens_map.json

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+}

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+size 2464616

tokenizer_config.json

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+  "tokenizer_class": "DebertaV2Tokenizer",
+  "unk_token": "[UNK]",
+  "vocab_type": "spm"
+}