---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dense
- generated_from_trainer
- dataset_size:3810
- loss:MultipleNegativesRankingLoss
base_model: distilbert/distilbert-base-uncased
widget:
- source_sentence: bmw ix
  sentences:
  - bmw ix
  - bmw x7
  - bmw i5
- source_sentence: bmw m2
  sentences:
  - bmw m2
  - tesla s
  - audi s5
- source_sentence: bmw xm
  sentences:
  - tesla y
  - bmw xm
  - audi rs
- source_sentence: bmw m8
  sentences:
  - bmw m8
  - audi c7
  - audi a3
- source_sentence: byd e6
  sentences:
  - audi r8
  - kia ev6
  - byd e6
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: make model dev
      type: make-model-dev
    metrics:
    - type: pearson_cosine
      value: 0.8766202507026644
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.8416717372995727
      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). It maps inputs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, 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:** 32 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
- **Supported Modality:** Text
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->

### Model Sources

- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/huggingface/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)

### Full Model Architecture

```
SentenceTransformer(
  (0): Transformer({'transformer_task': 'feature-extraction', 'modality_config': {'text': {'method': 'forward', 'method_output_name': 'last_hidden_state'}}, 'module_output_name': 'token_embeddings', 'architecture': 'DistilBertModel'})
  (1): Pooling({'embedding_dimension': 768, 'pooling_mode': 'mean', '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("SwarajSolanke-turtle/simcse-make-model-distilbert")
# Run inference
sentences = [
    'byd e6',
    'byd e6',
    'audi r8',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 1.0000, 0.3434],
#         [1.0000, 1.0000, 0.3434],
#         [0.3434, 0.3434, 1.0000]])
```
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## Evaluation

### Metrics

#### Semantic Similarity

* Dataset: `make-model-dev`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.sentence_transformer.evaluation.EmbeddingSimilarityEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| pearson_cosine      | 0.8766     |
| **spearman_cosine** | **0.8417** |

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## Training Details

### Training Dataset

#### Unnamed Dataset

* Size: 3,810 training samples
* Columns: <code>sentence1</code> and <code>sentence2</code>
* Approximate statistics based on the first 100 samples:
  |          | sentence1                                                                        | sentence2                                                                        |
  |:---------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
  | type     | string                                                                           | string                                                                           |
  | modality | text                                                                             | text                                                                             |
  | details  | <ul><li>min: 4 tokens</li><li>mean: 6.43 tokens</li><li>max: 12 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 6.43 tokens</li><li>max: 12 tokens</li></ul> |
* Samples:
  | sentence1                                | sentence2                                |
  |:-----------------------------------------|:-----------------------------------------|
  | <code>mahindra mahindra mm 550</code>    | <code>mahindra mahindra mm 550</code>    |
  | <code>tata motors aria</code>            | <code>tata motors aria</code>            |
  | <code>mg motor hector tomahawk ev</code> | <code>mg motor hector tomahawk ev</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",
      "gather_across_devices": false,
      "directions": [
          "query_to_doc"
      ],
      "partition_mode": "joint",
      "hardness_mode": null,
      "hardness_strength": 0.0
  }
  ```

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

- `per_device_train_batch_size`: 30
- `num_train_epochs`: 100
- `learning_rate`: 3e-05
- `warmup_steps`: 0.1
- `fp16`: True
- `batch_sampler`: no_duplicates

#### All Hyperparameters
<details><summary>Click to expand</summary>

- `per_device_train_batch_size`: 30
- `num_train_epochs`: 100
- `max_steps`: -1
- `learning_rate`: 3e-05
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: None
- `warmup_steps`: 0.1
- `optim`: adamw_torch_fused
- `optim_args`: None
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `optim_target_modules`: None
- `gradient_accumulation_steps`: 1
- `average_tokens_across_devices`: True
- `max_grad_norm`: 1.0
- `label_smoothing_factor`: 0.0
- `bf16`: False
- `fp16`: True
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `use_liger_kernel`: False
- `liger_kernel_config`: None
- `use_cache`: False
- `neftune_noise_alpha`: None
- `torch_empty_cache_steps`: None
- `auto_find_batch_size`: False
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `include_num_input_tokens_seen`: no
- `log_level`: passive
- `log_level_replica`: warning
- `disable_tqdm`: False
- `project`: huggingface
- `trackio_space_id`: None
- `trackio_bucket_id`: None
- `trackio_static_space_id`: None
- `per_device_eval_batch_size`: 8
- `prediction_loss_only`: True
- `eval_on_start`: False
- `eval_do_concat_batches`: True
- `eval_use_gather_object`: False
- `eval_accumulation_steps`: None
- `include_for_metrics`: []
- `batch_eval_metrics`: False
- `save_only_model`: False
- `save_on_each_node`: False
- `enable_jit_checkpoint`: False
- `push_to_hub`: False
- `hub_private_repo`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_always_push`: False
- `hub_revision`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `restore_callback_states_from_checkpoint`: False
- `full_determinism`: False
- `seed`: 42
- `data_seed`: None
- `use_cpu`: False
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `parallelism_config`: None
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `dataloader_prefetch_factor`: None
- `dataloader_multiprocessing_context`: None
- `dataloader_in_order`: True
- `remove_unused_columns`: True
- `label_names`: None
- `train_sampling_strategy`: random
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `ddp_static_graph`: None
- `ddp_backend`: None
- `ddp_timeout`: 1800
- `fsdp`: None
- `fsdp_config`: None
- `deepspeed`: None
- `debug`: []
- `skip_memory_metrics`: True
- `do_predict`: False
- `resume_from_checkpoint`: None
- `local_rank`: -1
- `prompts`: None
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
- `router_mapping`: {}
- `learning_rate_mapping`: {}
- `warmup_ratio`: None

</details>

### Training Logs
| Epoch | Step  | Training Loss | make-model-dev_spearman_cosine |
|:-----:|:-----:|:-------------:|:------------------------------:|
| -1    | -1    | -             | 0.8198                         |
| 5.0   | 635   | 0.0413        | -                              |
| 10.0  | 1270  | 0.0012        | 0.8535                         |
| 15.0  | 1905  | 0.0006        | -                              |
| 20.0  | 2540  | 0.0006        | 0.8349                         |
| 25.0  | 3175  | 0.0005        | -                              |
| 30.0  | 3810  | 0.0003        | 0.8346                         |
| 35.0  | 4445  | 0.0002        | -                              |
| 40.0  | 5080  | 0.0003        | 0.8460                         |
| 45.0  | 5715  | 0.0003        | -                              |
| 50.0  | 6350  | 0.0003        | 0.8307                         |
| 55.0  | 6985  | 0.0002        | -                              |
| 60.0  | 7620  | 0.0002        | 0.8426                         |
| 65.0  | 8255  | 0.0002        | -                              |
| 70.0  | 8890  | 0.0002        | 0.8389                         |
| 75.0  | 9525  | 0.0001        | -                              |
| 80.0  | 10160 | 0.0001        | 0.8402                         |
| 85.0  | 10795 | 0.0001        | -                              |
| 90.0  | 11430 | 0.0001        | 0.8393                         |
| 95.0  | 12065 | 0.0001        | -                              |
| 100.0 | 12700 | 0.0001        | 0.8417                         |
| -1    | -1    | -             | 0.8417                         |


### Training Time
- **Training**: 9.5 minutes
- **Evaluation**: 29.9 seconds
- **Total**: 10.0 minutes

### Framework Versions
- Python: 3.12.0
- Sentence Transformers: 6.1.0
- Transformers: 5.20.1
- PyTorch: 2.14.1+cu130
- Accelerate: 1.15.0
- Datasets: 5.1.0
- Tokenizers: 0.23.3

## Additional Resources

- [Training and Finetuning Embedding Models with Sentence Transformers](https://huggingface.co/blog/train-sentence-transformers): the end-to-end guide for training or finetuning Sentence Transformer models.
- [Introduction to Matryoshka Embedding Models](https://huggingface.co/blog/matryoshka): variable-size embeddings that can be truncated with minimal quality loss.
- [Binary and Scalar Embedding Quantization for Significantly Faster & Cheaper Retrieval](https://huggingface.co/blog/embedding-quantization): post-training compression of embedding vectors.
- [Multimodal Embedding & Reranker Models with Sentence Transformers](https://huggingface.co/blog/multimodal-sentence-transformers): use text, image, audio, and video models through the same API.
- [Training and Finetuning Multimodal Embedding & Reranker Models with Sentence Transformers](https://huggingface.co/blog/train-multimodal-sentence-transformers): train multimodal embedding models, with a Visual Document Retrieval walkthrough.

## 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{oord2019representationlearningcontrastivepredictive,
      title={Representation Learning with Contrastive Predictive Coding},
      author={Aaron van den Oord and Yazhe Li and Oriol Vinyals},
      year={2019},
      eprint={1807.03748},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/1807.03748},
}
```

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