---
language:
- en
library_name: sentence-transformers
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dataset_size:100K<n<1M
- loss:MultipleNegativesRankingLoss
base_model: microsoft/deberta-v3-base
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: ' Then he ran.'
  sentences:
  - He then started to run.
  - A man falls down onto his bed
  - The new rights are nice enough
- source_sentence: Tuppence rose.
  sentences:
  - Tuppence stood up.
  - A farmer tends to his chores.
  - (It may resemble Dungeons &amp
- source_sentence: 'The family. '
  sentences:
  - A family smiling on the couch.
  - A person is selling coconuts.
  - Derry had a passion for cybernetics.
- source_sentence: daddy is old
  sentences:
  - A farmer works on their farm.
  - I'm not sure about anything.
  - Most studies are not planned well.
- source_sentence: Are you sure?
  sentences:
  - Are you certain?
  - Both of the men are walking.
  - Things all close down at 11:30pm.
pipeline_tag: sentence-similarity
model-index:
- name: SentenceTransformer based on microsoft/deberta-v3-base
  results:
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: Unknown
      type: unknown
    metrics:
    - type: pearson_cosine
      value: 0.35614155568929684
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.4042062369647017
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.44470114795339144
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.464389588301289
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.4073816956345048
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.42806381869427496
      name: Spearman Euclidean
    - type: pearson_dot
      value: -0.033633706160895414
      name: Pearson Dot
    - type: spearman_dot
      value: -0.026115764956036586
      name: Spearman Dot
    - type: pearson_max
      value: 0.44470114795339144
      name: Pearson Max
    - type: spearman_max
      value: 0.464389588301289
      name: Spearman Max
  - task:
      type: binary-classification
      name: Binary Classification
    dataset:
      name: Unknown
      type: unknown
    metrics:
    - type: cosine_accuracy
      value: 0.6743498369853665
      name: Cosine Accuracy
    - type: cosine_accuracy_threshold
      value: 0.6929705142974854
      name: Cosine Accuracy Threshold
    - type: cosine_f1
      value: 0.7185155022977139
      name: Cosine F1
    - type: cosine_f1_threshold
      value: 0.5343961715698242
      name: Cosine F1 Threshold
    - type: cosine_precision
      value: 0.597061098221191
      name: Cosine Precision
    - type: cosine_recall
      value: 0.9020008762961881
      name: Cosine Recall
    - type: cosine_ap
      value: 0.7444738402602906
      name: Cosine Ap
    - type: dot_accuracy
      value: 0.6597164303586321
      name: Dot Accuracy
    - type: dot_accuracy_threshold
      value: 273.9874267578125
      name: Dot Accuracy Threshold
    - type: dot_f1
      value: 0.7170547216641059
      name: Dot F1
    - type: dot_f1_threshold
      value: 223.5421600341797
      name: Dot F1 Threshold
    - type: dot_precision
      value: 0.6021836228287841
      name: Dot Precision
    - type: dot_recall
      value: 0.8860814955454944
      name: Dot Recall
    - type: dot_ap
      value: 0.7124141445564182
      name: Dot Ap
    - type: manhattan_accuracy
      value: 0.6607779209947684
      name: Manhattan Accuracy
    - type: manhattan_accuracy_threshold
      value: 318.30352783203125
      name: Manhattan Accuracy Threshold
    - type: manhattan_f1
      value: 0.7059657412876551
      name: Manhattan F1
    - type: manhattan_f1_threshold
      value: 386.4444274902344
      name: Manhattan F1 Threshold
    - type: manhattan_precision
      value: 0.5926807497768521
      name: Manhattan Precision
    - type: manhattan_recall
      value: 0.8727910033591354
      name: Manhattan Recall
    - type: manhattan_ap
      value: 0.7339448128753968
      name: Manhattan Ap
    - type: euclidean_accuracy
      value: 0.6594131473197361
      name: Euclidean Accuracy
    - type: euclidean_accuracy_threshold
      value: 15.694759368896484
      name: Euclidean Accuracy Threshold
    - type: euclidean_f1
      value: 0.7072623190032551
      name: Euclidean F1
    - type: euclidean_f1_threshold
      value: 21.009502410888672
      name: Euclidean F1 Threshold
    - type: euclidean_precision
      value: 0.5743323306900009
      name: Euclidean Precision
    - type: euclidean_recall
      value: 0.9202570468818461
      name: Euclidean Recall
    - type: euclidean_ap
      value: 0.7342649046575551
      name: Euclidean Ap
    - type: max_accuracy
      value: 0.6743498369853665
      name: Max Accuracy
    - type: max_accuracy_threshold
      value: 318.30352783203125
      name: Max Accuracy Threshold
    - type: max_f1
      value: 0.7185155022977139
      name: Max F1
    - type: max_f1_threshold
      value: 386.4444274902344
      name: Max F1 Threshold
    - type: max_precision
      value: 0.6021836228287841
      name: Max Precision
    - type: max_recall
      value: 0.9202570468818461
      name: Max Recall
    - type: max_ap
      value: 0.7444738402602906
      name: Max Ap
---

# SentenceTransformer based on microsoft/deberta-v3-base

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) 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-base](https://huggingface.co/microsoft/deberta-v3-base) <!-- at revision 8ccc9b6f36199bec6961081d44eb72fb3f7353f3 -->
- **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-large-SentenceTransformer-0.01n")
# Run inference
sentences = [
    'Are you sure?',
    'Are you certain?',
    'Both of the men are walking.',
]
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>
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### Out-of-Scope Use

*List how the model may foreseeably be misused and address what users ought not to do with the model.*
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## 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      | 0.3561     |
| **spearman_cosine** | **0.4042** |
| pearson_manhattan   | 0.4447     |
| spearman_manhattan  | 0.4644     |
| pearson_euclidean   | 0.4074     |
| spearman_euclidean  | 0.4281     |
| pearson_dot         | -0.0336    |
| spearman_dot        | -0.0261    |
| pearson_max         | 0.4447     |
| spearman_max        | 0.4644     |

#### 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              | 0.6743     |
| cosine_accuracy_threshold    | 0.693      |
| cosine_f1                    | 0.7185     |
| cosine_f1_threshold          | 0.5344     |
| cosine_precision             | 0.5971     |
| cosine_recall                | 0.902      |
| cosine_ap                    | 0.7445     |
| dot_accuracy                 | 0.6597     |
| dot_accuracy_threshold       | 273.9874   |
| dot_f1                       | 0.7171     |
| dot_f1_threshold             | 223.5422   |
| dot_precision                | 0.6022     |
| dot_recall                   | 0.8861     |
| dot_ap                       | 0.7124     |
| manhattan_accuracy           | 0.6608     |
| manhattan_accuracy_threshold | 318.3035   |
| manhattan_f1                 | 0.706      |
| manhattan_f1_threshold       | 386.4444   |
| manhattan_precision          | 0.5927     |
| manhattan_recall             | 0.8728     |
| manhattan_ap                 | 0.7339     |
| euclidean_accuracy           | 0.6594     |
| euclidean_accuracy_threshold | 15.6948    |
| euclidean_f1                 | 0.7073     |
| euclidean_f1_threshold       | 21.0095    |
| euclidean_precision          | 0.5743     |
| euclidean_recall             | 0.9203     |
| euclidean_ap                 | 0.7343     |
| max_accuracy                 | 0.6743     |
| max_accuracy_threshold       | 318.3035   |
| max_f1                       | 0.7185     |
| max_f1_threshold             | 386.4444   |
| max_precision                | 0.6022     |
| max_recall                   | 0.9203     |
| **max_ap**                   | **0.7445** |

<!--
## Bias, Risks and Limitations

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### Recommendations

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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: 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>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

### 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: 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>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

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

- `eval_strategy`: steps
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 64
- `num_train_epochs`: 5
- `lr_scheduler_type`: cosine
- `warmup_ratio`: 0.25
- `save_safetensors`: False
- `fp16`: True
- `push_to_hub`: True
- `hub_model_id`: bobox/DeBERTaV3-large-SentenceTransformer-0.01n
- `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`: 64
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_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`: 5
- `max_steps`: -1
- `lr_scheduler_type`: cosine
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.25
- `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-large-SentenceTransformer-0.01n
- `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     | -             | 3.2007 | 0.5917 | 0.4042          |
| 0.1250 | 1228  | 2.3115        | 1.3295 | 0.6783 | -               |
| 0.2500 | 2456  | 1.1344        | 1.0007 | 0.7048 | -               |
| 0.3750 | 3684  | 0.9827        | 0.8551 | 0.7091 | -               |
| 0.5001 | 4912  | 0.9045        | 0.7483 | 0.7148 | -               |
| 0.6251 | 6140  | 0.6488        | 0.6057 | 0.7276 | -               |
| 0.7501 | 7368  | 0.1224        | 0.6683 | 0.7358 | -               |
| 0.8751 | 8596  | 0.1063        | 0.6895 | 0.7294 | -               |
| 1.0001 | 9824  | 0.0959        | 0.7052 | 0.7325 | -               |
| 1.1251 | 11052 | 0.8908        | 0.5446 | 0.7343 | -               |
| 1.2501 | 12280 | 0.8212        | 0.5973 | 0.7225 | -               |
| 1.3751 | 13508 | 0.7871        | 0.6420 | 0.7078 | -               |
| 1.5002 | 14736 | 0.7547        | 0.6057 | 0.7135 | -               |
| 1.6252 | 15964 | 0.5267        | 0.5308 | 0.7370 | -               |
| 1.7502 | 17192 | 0.0619        | 0.5269 | 0.7348 | -               |
| 1.8752 | 18420 | 0.0509        | 0.5550 | 0.7378 | -               |
| 2.0002 | 19648 | 0.044         | 0.5835 | 0.7359 | -               |
| 2.1252 | 20876 | 0.7125        | 0.4636 | 0.7351 | -               |
| 2.2502 | 22104 | 0.6542        | 0.4919 | 0.7358 | -               |
| 2.3752 | 23332 | 0.6273        | 0.4758 | 0.7219 | -               |
| 2.5003 | 24560 | 0.5972        | 0.4816 | 0.7279 | -               |
| 2.6253 | 25788 | 0.3981        | 0.4526 | 0.7463 | -               |
| 2.7503 | 27016 | 0.0169        | 0.4568 | 0.7446 | -               |
| 2.8753 | 28244 | 0.0142        | 0.4995 | 0.7445 | -               |


### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.0.0
- Transformers: 4.41.2
- PyTorch: 2.3.0+cu121
- 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",
}
```

#### 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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