README.md

---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:4480
- loss:CosineSimilarityLoss
base_model: distilbert/distilbert-base-uncased
widget:
- source_sentence: I have the same thing.
  sentences:
  - And, Obama gets zero credit for the budget under him.
  - UK urges countries over Syria aid
  - I have the same situation and have traveled extensively.
- source_sentence: a man wearing a gray hat fishing out of a fishing boat.
  sentences:
  - A man wearing a straw hat and fishing vest in a stream.
  - no, it's not an answer.
  - Mann's work and the HS was all about Tree rings.
- source_sentence: A small white cat with glowing eyes standing underneath a chair.
  sentences:
  - A white cat stands on the floor.
  - A woman is cutting a tomato.
  - The man is playing the piano with his nose.
- source_sentence: Originally Posted by muslim girl ooops sorry!
  sentences:
  - Originally Posted by muslim girl its not a complete impossibility.
  - A person riding a dirt bike.
  - None of the casualties was Americans, said Capt. Michael Calvert, regiment spokesman.
- source_sentence: Tell us what the charges were.
  sentences:
  - The Judges orders a three-page letter to be filed.
  - Yes what are his charges.
  - A person is buttering a tray.
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- pearson_cosine
- spearman_cosine
model-index:
- name: SentenceTransformer based on distilbert/distilbert-base-uncased
  results:
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts dev
      type: sts-dev
    metrics:
    - type: pearson_cosine
      value: 0.3779858984516553
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.473144636361867
      name: Spearman Cosine
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts test
      type: sts-test
    metrics:
    - type: pearson_cosine
      value: 0.34896468808057485
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.44906241393019836
      name: Spearman Cosine
---

# SentenceTransformer based on distilbert/distilbert-base-uncased

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on the csv 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:** [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) <!-- at revision 12040accade4e8a0f71eabdb258fecc2e7e948be -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
    - csv
<!-- - **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: DistilBertModel 
  (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("Pyro-X2/distilbert-base-uncased-sts")
# Run inference
sentences = [
    'Tell us what the charges were.',
    'Yes what are his charges.',
    'A person is buttering a tray.',
]
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

#### Semantic Similarity

* Datasets: `sts-dev` and `sts-test`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

| Metric              | sts-dev    | sts-test   |
|:--------------------|:-----------|:-----------|
| pearson_cosine      | 0.378      | 0.349      |
| **spearman_cosine** | **0.4731** | **0.4491** |

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## Training Details

### Training Dataset

#### csv

* Dataset: csv
* Size: 4,480 training 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                                                                            | int                                                                                                                         |
  | details | <ul><li>min: 6 tokens</li><li>mean: 15.14 tokens</li><li>max: 50 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 15.07 tokens</li><li>max: 52 tokens</li></ul> | <ul><li>0: ~14.20%</li><li>1: ~11.60%</li><li>2: ~18.40%</li><li>3: ~23.30%</li><li>4: ~21.70%</li><li>5: ~10.80%</li></ul> |
* Samples:
  | sentence1                                                            | sentence2                                                                     | score          |
  |:---------------------------------------------------------------------|:------------------------------------------------------------------------------|:---------------|
  | <code>A man is speaking.</code>                                      | <code>A man is spitting.</code>                                               | <code>1</code> |
  | <code>Austrian found hoarding 56 stolen skulls in home museum</code> | <code>Austrian man charged after 56 human skulls are found at his home</code> | <code>4</code> |
  | <code>Mitt Romney wins Republican primary in Indiana</code>          | <code>Romney wins Florida Republican primary</code>                           | <code>2</code> |
* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
  ```json
  {
      "loss_fct": "torch.nn.modules.loss.MSELoss"
  }
  ```

### Evaluation Dataset

#### csv

* Dataset: csv
* Size: 560 evaluation samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
* Approximate statistics based on the first 560 samples:
  |         | sentence1                                                                         | sentence2                                                                         | score                                                                                                                       |
  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                            | int                                                                                                                         |
  | details | <ul><li>min: 5 tokens</li><li>mean: 14.41 tokens</li><li>max: 44 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 14.28 tokens</li><li>max: 42 tokens</li></ul> | <ul><li>0: ~12.86%</li><li>1: ~16.96%</li><li>2: ~14.82%</li><li>3: ~18.21%</li><li>4: ~26.43%</li><li>5: ~10.71%</li></ul> |
* Samples:
  | sentence1                                                                         | sentence2                                                           | score          |
  |:----------------------------------------------------------------------------------|:--------------------------------------------------------------------|:---------------|
  | <code>An airplane is flying in the air.</code>                                    | <code>A South African Airways plane is flying in a blue sky.</code> | <code>3</code> |
  | <code>A television, upholstered chair, and coffee stable in a bright room.</code> | <code>A leather couch and wooden table in a living room.</code>     | <code>2</code> |
  | <code>Red panda’s short-lived zoo escape</code>                                 | <code>India’s march to Mars</code>                                | <code>0</code> |
* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
  ```json
  {
      "loss_fct": "torch.nn.modules.loss.MSELoss"
  }
  ```

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

- `eval_strategy`: steps
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `num_train_epochs`: 4
- `warmup_ratio`: 0.1

#### 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`: 1.0
- `num_train_epochs`: 4
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `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`: None
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `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
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
| Epoch  | Step | Training Loss | Validation Loss | sts-dev_spearman_cosine | sts-test_spearman_cosine |
|:------:|:----:|:-------------:|:---------------:|:-----------------------:|:------------------------:|
| 0.3571 | 100  | 5.031         | 5.0990          | 0.4973                  | -                        |
| 0.7143 | 200  | 4.9152        | 5.0985          | 0.4944                  | -                        |
| 1.0714 | 300  | 4.8198        | 5.0984          | 0.4959                  | -                        |
| 1.4286 | 400  | 4.9102        | 5.0983          | 0.4884                  | -                        |
| 1.7857 | 500  | 4.9238        | 5.0983          | 0.4798                  | -                        |
| 2.1429 | 600  | 4.9387        | 5.0983          | 0.4777                  | -                        |
| 2.5    | 700  | 4.8955        | 5.0983          | 0.4752                  | -                        |
| 2.8571 | 800  | 4.9623        | 5.0983          | 0.4740                  | -                        |
| 3.2143 | 900  | 4.7754        | 5.0983          | 0.4739                  | -                        |
| 3.5714 | 1000 | 4.936         | 5.0983          | 0.4734                  | -                        |
| 3.9286 | 1100 | 4.9254        | 5.0983          | 0.4731                  | -                        |
| -1     | -1   | -             | -               | -                       | 0.4491                   |


### Framework Versions
- Python: 3.12.12
- Sentence Transformers: 4.1.0
- Transformers: 4.49.0
- PyTorch: 2.3.0.post101
- Accelerate: 1.10.1
- Datasets: 3.3.2
- Tokenizers: 0.21.0

## 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",
}
```

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