README.md

---
base_model: cross-encoder/nli-deberta-v3-large
library_name: sentence-transformers
metrics:
- cosine_accuracy@1
- cosine_accuracy@3
- cosine_accuracy@5
- cosine_accuracy@10
- cosine_precision@1
- cosine_precision@3
- cosine_precision@5
- cosine_precision@10
- cosine_recall@1
- cosine_recall@3
- cosine_recall@5
- cosine_recall@10
- cosine_ndcg@10
- cosine_mrr@10
- cosine_map@100
- dot_accuracy@1
- dot_accuracy@3
- dot_accuracy@5
- dot_accuracy@10
- dot_precision@1
- dot_precision@3
- dot_precision@5
- dot_precision@10
- dot_recall@1
- dot_recall@3
- dot_recall@5
- dot_recall@10
- dot_ndcg@10
- dot_mrr@10
- dot_map@100
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:40338
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: '"Rumpelstilsken, I command the sun to set!"  He seemed to sense
    a hesitation in his mind, and then the impression of jeweled gears turning.'
  sentences:
  - A football game is playing.
  - He sensed hesitation when commanding Rumpelstiltskin.
  - I ran and he saw me immediately.
- source_sentence: A woman wears sunglasses and a black coat as she walks.
  sentences:
  - The lady in black walks while wearing her shades.
  - Two women were walking
  - The people are running towards the mountains.
- source_sentence: The Congress relies on GAO to examine virtually every federal program,
    activity, and policy, as well as institutions that rely on federal funds.
  sentences:
  - The men are standing in line at the restaurant.
  - GAO helps Congress.
  - Tide permitting, view the shrine from its base to appreciate its full size.
- source_sentence: The resort was named after Louis James Fraser, an English adventurer
    and scoundrel, who dealt in mule hides, tin, opium, and gambling.
  sentences:
  - A man in front of people.
  - The resort was named after an English adventurer and scoundrel.
  - A woman is holding flowers by two men on a bench.
- source_sentence: Three men riding a bicycle, tow of them are wearing a helmet.
  sentences:
  - Accountability measures help establish the financial condition of the government.
  - A man is pushing a truck.
  - There are at least two helmets.
model-index:
- name: SentenceTransformer based on cross-encoder/nli-deberta-v3-large
  results:
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: eval
      type: eval
    metrics:
    - type: cosine_accuracy@1
      value: 0.0003470672814715653
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.2842728940453171
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.42875204521790866
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.5317318657345431
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.0003470672814715653
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.09475763134843902
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.08575040904358174
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.053173186573454316
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.0003470672814715653
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.2842728940453171
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.42875204521790866
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.5317318657345431
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.2599623819220365
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.17320152646642903
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.1849889511878054
      name: Cosine Map@100
    - type: dot_accuracy@1
      value: 0.003718578015766771
      name: Dot Accuracy@1
    - type: dot_accuracy@3
      value: 0.262531607913134
      name: Dot Accuracy@3
    - type: dot_accuracy@5
      value: 0.40182954038375723
      name: Dot Accuracy@5
    - type: dot_accuracy@10
      value: 0.5089741682780504
      name: Dot Accuracy@10
    - type: dot_precision@1
      value: 0.003718578015766771
      name: Dot Precision@1
    - type: dot_precision@3
      value: 0.08751053597104465
      name: Dot Precision@3
    - type: dot_precision@5
      value: 0.08036590807675144
      name: Dot Precision@5
    - type: dot_precision@10
      value: 0.050897416827805034
      name: Dot Precision@10
    - type: dot_recall@1
      value: 0.003718578015766771
      name: Dot Recall@1
    - type: dot_recall@3
      value: 0.262531607913134
      name: Dot Recall@3
    - type: dot_recall@5
      value: 0.40182954038375723
      name: Dot Recall@5
    - type: dot_recall@10
      value: 0.5089741682780504
      name: Dot Recall@10
    - type: dot_ndcg@10
      value: 0.24760156704826422
      name: Dot Ndcg@10
    - type: dot_mrr@10
      value: 0.16454750021051548
      name: Dot Mrr@10
    - type: dot_map@100
      value: 0.17684391661589097
      name: Dot Map@100
---

# SentenceTransformer based on cross-encoder/nli-deberta-v3-large

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [cross-encoder/nli-deberta-v3-large](https://huggingface.co/cross-encoder/nli-deberta-v3-large). It maps sentences & paragraphs to a 1024-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:** [cross-encoder/nli-deberta-v3-large](https://huggingface.co/cross-encoder/nli-deberta-v3-large) <!-- at revision 52fab31a566138fbd1f6833a4efc1199f875f05e -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 1024 tokens
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **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': 1024, '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("richie-ghost/sbert_ft_cross-encoder-nli-deberta-v3-large")
# Run inference
sentences = [
    'Three men riding a bicycle, tow of them are wearing a helmet.',
    'There are at least two helmets.',
    'Accountability measures help establish the financial condition of the government.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```

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## Evaluation

### Metrics

#### Information Retrieval
* Dataset: `eval`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)

| Metric              | Value     |
|:--------------------|:----------|
| cosine_accuracy@1   | 0.0003    |
| cosine_accuracy@3   | 0.2843    |
| cosine_accuracy@5   | 0.4288    |
| cosine_accuracy@10  | 0.5317    |
| cosine_precision@1  | 0.0003    |
| cosine_precision@3  | 0.0948    |
| cosine_precision@5  | 0.0858    |
| cosine_precision@10 | 0.0532    |
| cosine_recall@1     | 0.0003    |
| cosine_recall@3     | 0.2843    |
| cosine_recall@5     | 0.4288    |
| cosine_recall@10    | 0.5317    |
| cosine_ndcg@10      | 0.26      |
| cosine_mrr@10       | 0.1732    |
| **cosine_map@100**  | **0.185** |
| dot_accuracy@1      | 0.0037    |
| dot_accuracy@3      | 0.2625    |
| dot_accuracy@5      | 0.4018    |
| dot_accuracy@10     | 0.509     |
| dot_precision@1     | 0.0037    |
| dot_precision@3     | 0.0875    |
| dot_precision@5     | 0.0804    |
| dot_precision@10    | 0.0509    |
| dot_recall@1        | 0.0037    |
| dot_recall@3        | 0.2625    |
| dot_recall@5        | 0.4018    |
| dot_recall@10       | 0.509     |
| dot_ndcg@10         | 0.2476    |
| dot_mrr@10          | 0.1645    |
| dot_map@100         | 0.1768    |

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## Training Details

### Training Dataset

#### Unnamed Dataset


* Size: 40,338 training samples
* Columns: <code>sentence_0</code> and <code>sentence_1</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence_0                                                                         | sentence_1                                                                        |
  |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                            |
  | details | <ul><li>min: 5 tokens</li><li>mean: 19.64 tokens</li><li>max: 129 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 11.27 tokens</li><li>max: 36 tokens</li></ul> |
* Samples:
  | sentence_0                                                                             | sentence_1                                                  |
  |:---------------------------------------------------------------------------------------|:------------------------------------------------------------|
  | <code>A group of ladies trying to learn how to belly dance.</code>                     | <code>Several women learn the art of exotic dancing.</code> |
  | <code>A man and a woman are having a conversation, while the man drinks a beer.</code> | <code>The man is drinking.</code>                           |
  | <code>A brown dog drinks from a water bottle.</code>                                   | <code>A brown cat drinks from a bowl.</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`: 16
- `per_device_eval_batch_size`: 16
- `num_train_epochs`: 4
- `multi_dataset_batch_sampler`: round_robin

#### 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`: 16
- `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
- `torch_empty_cache_steps`: None
- `learning_rate`: 5e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1
- `num_train_epochs`: 4
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.0
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: 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
- `eval_on_start`: False
- `eval_use_gather_object`: False
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: round_robin

</details>

### Training Logs
| Epoch  | Step  | Training Loss | eval_cosine_map@100 |
|:------:|:-----:|:-------------:|:-------------------:|
| 0.1983 | 500   | 1.2356        | 0.0873              |
| 0.3965 | 1000  | 0.4077        | 0.1200              |
| 0.5948 | 1500  | 0.3205        | 0.1280              |
| 0.7930 | 2000  | 0.2576        | 0.1416              |
| 0.9913 | 2500  | 0.2435        | 0.1476              |
| 1.0    | 2522  | -             | 0.1492              |
| 1.1895 | 3000  | 0.1821        | 0.1553              |
| 1.3878 | 3500  | 0.1237        | 0.1589              |
| 1.5860 | 4000  | 0.1074        | 0.1603              |
| 1.7843 | 4500  | 0.0905        | 0.1654              |
| 1.9826 | 5000  | 0.0783        | 0.1685              |
| 2.0    | 5044  | -             | 0.1683              |
| 2.1808 | 5500  | 0.0583        | 0.1698              |
| 2.3791 | 6000  | 0.0432        | 0.1746              |
| 2.5773 | 6500  | 0.0365        | 0.1749              |
| 2.7756 | 7000  | 0.0303        | 0.1791              |
| 2.9738 | 7500  | 0.0276        | 0.1788              |
| 3.0    | 7566  | -             | 0.1805              |
| 3.1721 | 8000  | 0.02          | 0.1807              |
| 3.3703 | 8500  | 0.013         | 0.1823              |
| 3.5686 | 9000  | 0.0123        | 0.1839              |
| 3.7669 | 9500  | 0.0099        | 0.1852              |
| 3.9651 | 10000 | 0.01          | 0.1850              |
| 4.0    | 10088 | -             | 0.1850              |


### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.2.1
- Transformers: 4.44.2
- PyTorch: 2.5.0+cu121
- Accelerate: 1.0.1
- Datasets: 3.0.2
- 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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