sentence-transformers pytorch deberta-v2 sentence-similarity feature-extraction generated_from_trainer dataset_size:67190 loss:AdaptiveLayerLoss loss:MultipleNegativesRankingLoss en dataset:stanfordnlp/snli arxiv:1908.10084 arxiv:2402.14776 arxiv:1705.00652 base_model:microsoft/deberta-v3-small base_model:finetune:microsoft/deberta-v3-small model-index autotrain_compatible endpoints_compatible region:us
---
base_model: microsoft/deberta-v3-small
datasets:
- stanfordnlp/snli
language:
- en
library_name: sentence-transformers
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
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:67190
- loss:AdaptiveLayerLoss
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: A man is walking past a large sign that says E.S.E. Electronics.
  sentences:
  - a child opens a present on his birthday
  - The man works at E.S.E Electronics.
  - The soccer team in blue plays soccer.
- source_sentence: This child is on the library steps.
  sentences:
  - A mother dog checking up on her baby puppy.
  - A guy bites into a freshly opened marshmallow chick
  - The child is on the steps inside the library.
- source_sentence: Two men are standing in a boat.
  sentences:
  - People are watching the flowers blossom
  - The couple is married.
  - A few men are fishing on a boat.
- source_sentence: Four men playing drums in very orange lighting while one of them
    is also drinking something out of a bottle.
  sentences:
  - four men play drums
  - The man puts something on the other mans head.
  - The dogs are in the backyard.
- source_sentence: First Lady Laura Bush at podium, in front of seated audience, at
    the White House Conference on Global Literacy.
  sentences:
  - Some people are exercising outside.
  - The former First Lady is at the podium for a conference.
  - This person is going to the waterfall
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: 0.6651071536371869
      name: Cosine Accuracy
    - type: cosine_accuracy_threshold
      value: 0.687929630279541
      name: Cosine Accuracy Threshold
    - type: cosine_f1
      value: 0.7077349458301839
      name: Cosine F1
    - type: cosine_f1_threshold
      value: 0.6304811239242554
      name: Cosine F1 Threshold
    - type: cosine_precision
      value: 0.6222862206468763
      name: Cosine Precision
    - type: cosine_recall
      value: 0.8203855140186916
      name: Cosine Recall
    - type: cosine_ap
      value: 0.7058220689813709
      name: Cosine Ap
    - type: dot_accuracy
      value: 0.6313009357078176
      name: Dot Accuracy
    - type: dot_accuracy_threshold
      value: 135.98495483398438
      name: Dot Accuracy Threshold
    - type: dot_f1
      value: 0.6997334569475027
      name: Dot F1
    - type: dot_f1_threshold
      value: 115.54609680175781
      name: Dot F1 Threshold
    - type: dot_precision
      value: 0.5800192122958694
      name: Dot Precision
    - type: dot_recall
      value: 0.8817172897196262
      name: Dot Recall
    - type: dot_ap
      value: 0.6554755795160082
      name: Dot Ap
    - type: manhattan_accuracy
      value: 0.6708421370359191
      name: Manhattan Accuracy
    - type: manhattan_accuracy_threshold
      value: 219.32388305664062
      name: Manhattan Accuracy Threshold
    - type: manhattan_f1
      value: 0.7119951778179626
      name: Manhattan F1
    - type: manhattan_f1_threshold
      value: 262.314697265625
      name: Manhattan F1 Threshold
    - type: manhattan_precision
      value: 0.6062410182714022
      name: Manhattan Precision
    - type: manhattan_recall
      value: 0.8624415887850467
      name: Manhattan Recall
    - type: manhattan_ap
      value: 0.7135236162968746
      name: Manhattan Ap
    - type: euclidean_accuracy
      value: 0.6652580742529429
      name: Euclidean Accuracy
    - type: euclidean_accuracy_threshold
      value: 11.506816864013672
      name: Euclidean Accuracy Threshold
    - type: euclidean_f1
      value: 0.7080090384132564
      name: Euclidean F1
    - type: euclidean_f1_threshold
      value: 12.478536605834961
      name: Euclidean F1 Threshold
    - type: euclidean_precision
      value: 0.6208718626155878
      name: Euclidean Precision
    - type: euclidean_recall
      value: 0.8235981308411215
      name: Euclidean Recall
    - type: euclidean_ap
      value: 0.7090362803652147
      name: Euclidean Ap
    - type: max_accuracy
      value: 0.6708421370359191
      name: Max Accuracy
    - type: max_accuracy_threshold
      value: 219.32388305664062
      name: Max Accuracy Threshold
    - type: max_f1
      value: 0.7119951778179626
      name: Max F1
    - type: max_f1_threshold
      value: 262.314697265625
      name: Max F1 Threshold
    - type: max_precision
      value: 0.6222862206468763
      name: Max Precision
    - type: max_recall
      value: 0.8817172897196262
      name: Max Recall
    - type: max_ap
      value: 0.7135236162968746
      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-ST-AdaptiveLayer-Norm-ep2")
# Run inference
sentences = [
    'First Lady Laura Bush at podium, in front of seated audience, at the White House Conference on Global Literacy.',
    'The former First Lady is at the podium for a conference.',
    'This person is going to the waterfall',
]
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]
```

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## 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              | 0.6651     |
| cosine_accuracy_threshold    | 0.6879     |
| cosine_f1                    | 0.7077     |
| cosine_f1_threshold          | 0.6305     |
| cosine_precision             | 0.6223     |
| cosine_recall                | 0.8204     |
| cosine_ap                    | 0.7058     |
| dot_accuracy                 | 0.6313     |
| dot_accuracy_threshold       | 135.985    |
| dot_f1                       | 0.6997     |
| dot_f1_threshold             | 115.5461   |
| dot_precision                | 0.58       |
| dot_recall                   | 0.8817     |
| dot_ap                       | 0.6555     |
| manhattan_accuracy           | 0.6708     |
| manhattan_accuracy_threshold | 219.3239   |
| manhattan_f1                 | 0.712      |
| manhattan_f1_threshold       | 262.3147   |
| manhattan_precision          | 0.6062     |
| manhattan_recall             | 0.8624     |
| manhattan_ap                 | 0.7135     |
| euclidean_accuracy           | 0.6653     |
| euclidean_accuracy_threshold | 11.5068    |
| euclidean_f1                 | 0.708      |
| euclidean_f1_threshold       | 12.4785    |
| euclidean_precision          | 0.6209     |
| euclidean_recall             | 0.8236     |
| euclidean_ap                 | 0.709      |
| max_accuracy                 | 0.6708     |
| max_accuracy_threshold       | 219.3239   |
| max_f1                       | 0.712      |
| max_f1_threshold             | 262.3147   |
| max_precision                | 0.6223     |
| max_recall                   | 0.8817     |
| **max_ap**                   | **0.7135** |

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## 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: 67,190 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: 4 tokens</li><li>mean: 21.19 tokens</li><li>max: 133 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 11.77 tokens</li><li>max: 49 tokens</li></ul> | <ul><li>0: 100.00%</li></ul> |
* Samples:
  | sentence1                                                                                                                              | sentence2                                                                                        | label          |
  |:---------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------|:---------------|
  | <code>Without a placebo group, we still won't know if any of the treatments are better than nothing and therefore worth giving.</code> | <code>It is necessary to use a controlled method to ensure the treatments are worthwhile.</code> | <code>0</code> |
  | <code>It was conducted in silence.</code>                                                                                              | <code>It was done silently.</code>                                                               | <code>0</code> |
  | <code>oh Lewisville  any decent food in your cafeteria up there</code>                                                                 | <code>Is there any decent food in your cafeteria up there in Lewisville?</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": 0.05,
      "kl_div_weight": 2,
      "kl_temperature": 0.9
  }
  ```

### Evaluation Dataset

#### stanfordnlp/snli

* Dataset: [stanfordnlp/snli](https://huggingface.co/datasets/stanfordnlp/snli) at [cdb5c3d](https://huggingface.co/datasets/stanfordnlp/snli/tree/cdb5c3d5eed6ead6e5a341c8e56e669bb666725b)
* Size: 6,626 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": 1,
      "prior_layers_weight": 0.05,
      "kl_div_weight": 2,
      "kl_temperature": 0.9
  }
  ```

### Training Hyperparameters
#### Non-Default Hyperparameters

- `eval_strategy`: steps
- `per_device_train_batch_size`: 45
- `per_device_eval_batch_size`: 22
- `learning_rate`: 3e-06
- `weight_decay`: 1e-09
- `num_train_epochs`: 2
- `lr_scheduler_type`: cosine
- `warmup_ratio`: 0.5
- `save_safetensors`: False
- `fp16`: True
- `push_to_hub`: True
- `hub_model_id`: bobox/DeBERTaV3-small-ST-AdaptiveLayer-Norm-ep2-checkpoints
- `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`: 45
- `per_device_eval_batch_size`: 22
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `learning_rate`: 3e-06
- `weight_decay`: 1e-09
- `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`: cosine
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.5
- `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-ST-AdaptiveLayer-Norm-ep2-checkpoints
- `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 |
|:------:|:----:|:-------------:|:------:|:------:|
| 0.1004 | 150  | 4.5827        | -      | -      |
| 0.2001 | 299  | -             | 3.5735 | 0.6133 |
| 0.2008 | 300  | 3.5451        | -      | -      |
| 0.3012 | 450  | 2.9066        | -      | -      |
| 0.4003 | 598  | -             | 2.8785 | 0.6561 |
| 0.4016 | 600  | 2.5141        | -      | -      |
| 0.5020 | 750  | 2.0248        | -      | -      |
| 0.6004 | 897  | -             | 2.1300 | 0.6917 |
| 0.6024 | 900  | 1.6782        | -      | -      |
| 0.7028 | 1050 | 1.4187        | -      | -      |
| 0.8005 | 1196 | -             | 1.7111 | 0.7051 |
| 0.8032 | 1200 | 1.2446        | -      | -      |
| 0.9036 | 1350 | 1.1078        | -      | -      |
| 1.0007 | 1495 | -             | 1.4859 | 0.7108 |
| 1.0040 | 1500 | 0.9827        | -      | -      |
| 1.1044 | 1650 | 0.9335        | -      | -      |
| 1.2008 | 1794 | -             | 1.3516 | 0.7121 |
| 1.2048 | 1800 | 0.8595        | -      | -      |
| 1.3052 | 1950 | 0.8362        | -      | -      |
| 1.4009 | 2093 | -             | 1.2659 | 0.7147 |
| 1.4056 | 2100 | 0.8167        | -      | -      |
| 1.5060 | 2250 | 0.7695        | -      | -      |
| 1.6011 | 2392 | -             | 1.2218 | 0.7135 |
| 1.6064 | 2400 | 0.7544        | -      | -      |
| 1.7068 | 2550 | 0.7625        | -      | -      |
| 1.8012 | 2691 | -             | 1.2073 | 0.7135 |
| 1.8072 | 2700 | 0.7366        | -      | -      |
| 1.9076 | 2850 | 0.7348        | -      | -      |


### 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}
}
```

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