---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dense
- generated_from_trainer
- dataset_size:227518
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
base_model: sentence-transformers/all-MiniLM-L6-v2
widget:
- source_sentence: UTU
  sentences:
  - < HOSIER, person who sells stockings, etc [n]
  - act of speaking foolishly [n]
  - reward [n]
- source_sentence: PROEMS
  sentences:
  - < PROEM, introduction or preface [n]
  - edge of a sea or lake [n] / prop or support [v]
  - wad (black earthy ore of manganese) [n]
- source_sentence: INSTITUTORS
  sentences:
  - < INSTITUTOR, one who institutes [n]
  - assembly of judges [n]
  - < FATE, power supposed to predetermine events [n]
- source_sentence: HAEMAGOGUES
  sentences:
  - < VIVISECTORIUM, a place for vivisection [n]
  - < GROTESQUE, strangely distorted [adj]
  - < HAEMAGOGUE, a drug that promotes the flow of blood [n]
- source_sentence: BOLDING
  sentences:
  - < NAUCH, nautch (intricate traditional Indian dance) [n]
  - < TABU, taboo (prohibition resulting from religious or social conventions) [n]
  - < BOLD, confident and fearless [adj]
pipeline_tag: sentence-similarity
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
model-index:
- name: SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2
  results:
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dictionary test
      type: dictionary-test
    metrics:
    - type: cosine_accuracy@1
      value: 0.5970332278481013
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.7252768987341772
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.7495648734177215
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.7743275316455697
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.5970332278481013
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.2417589662447257
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.14991297468354428
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.07743275316455696
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.5970332278481013
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.7252768987341772
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.7495648734177215
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.7743275316455697
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.6919377177591847
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.6648749560478296
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.6677242431561833
      name: Cosine Map@100
---

# SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) on the csv dataset. It maps sentences & paragraphs to a 384-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:** [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) <!-- at revision c9745ed1d9f207416be6d2e6f8de32d1f16199bf -->
- **Maximum Sequence Length:** 256 tokens
- **Output Dimensionality:** 384 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/huggingface/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': 256, 'do_lower_case': False, 'architecture': 'BertModel'})
  (1): Pooling({'word_embedding_dimension': 384, '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})
  (2): Normalize()
)
```

## 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("Mehularora/scrabble-embed-v1")
# Run inference
sentences = [
    'BOLDING',
    '< BOLD, confident and fearless [adj]',
    '< NAUCH, nautch (intricate traditional Indian dance) [n]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7391, 0.0112],
#         [0.7391, 1.0000, 0.0722],
#         [0.0112, 0.0722, 1.0000]])
```

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## Evaluation

### Metrics

#### Information Retrieval

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

| Metric              | Value      |
|:--------------------|:-----------|
| cosine_accuracy@1   | 0.597      |
| cosine_accuracy@3   | 0.7253     |
| cosine_accuracy@5   | 0.7496     |
| cosine_accuracy@10  | 0.7743     |
| cosine_precision@1  | 0.597      |
| cosine_precision@3  | 0.2418     |
| cosine_precision@5  | 0.1499     |
| cosine_precision@10 | 0.0774     |
| cosine_recall@1     | 0.597      |
| cosine_recall@3     | 0.7253     |
| cosine_recall@5     | 0.7496     |
| cosine_recall@10    | 0.7743     |
| **cosine_ndcg@10**  | **0.6919** |
| cosine_mrr@10       | 0.6649     |
| cosine_map@100      | 0.6677     |

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## Training Details

### Training Dataset

#### csv

* Dataset: csv
* Size: 227,518 training samples
* Columns: <code>word</code> and <code>definition</code>
* Approximate statistics based on the first 1000 samples:
  |         | word                                                                           | definition                                                                        |
  |:--------|:-------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                         | string                                                                            |
  | details | <ul><li>min: 3 tokens</li><li>mean: 4.9 tokens</li><li>max: 9 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 15.82 tokens</li><li>max: 44 tokens</li></ul> |
* Samples:
  | word                     | definition                                              |
  |:-------------------------|:--------------------------------------------------------|
  | <code>SLURPIEST</code>   | <code>< SLURPY, making a slurping noise [adj]</code>    |
  | <code>CRISPNESSES</code> | <code>< CRISPNESS, < CRISP, fresh and firm [adj]</code> |
  | <code>CECUTIENCY</code>  | <code>a tendency to blindness [n]</code>                |
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
  ```json
  {
      "loss": "MultipleNegativesRankingLoss",
      "matryoshka_dims": [
          384,
          256
      ],
      "matryoshka_weights": [
          1,
          1
      ],
      "n_dims_per_step": -1
  }
  ```

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

- `eval_strategy`: steps
- `per_device_train_batch_size`: 64
- `learning_rate`: 2e-05
- `num_train_epochs`: 1
- `fp16`: True

#### 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`: 64
- `per_device_eval_batch_size`: 8
- `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`: 2e-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`: 1
- `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
- `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}
- `parallelism_config`: None
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch_fused
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `project`: huggingface
- `trackio_space_id`: trackio
- `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
- `hub_revision`: None
- `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
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: no
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `liger_kernel_config`: None
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: True
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional
- `router_mapping`: {}
- `learning_rate_mapping`: {}

</details>

### Training Logs
| Epoch  | Step | Training Loss | dictionary-test_cosine_ndcg@10 |
|:------:|:----:|:-------------:|:------------------------------:|
| 0.0281 | 100  | 1.5353        | 0.6306                         |
| 0.0563 | 200  | 1.2836        | 0.6543                         |
| 0.0844 | 300  | 1.2305        | 0.6637                         |
| 0.1125 | 400  | 1.1669        | 0.6651                         |
| 0.1406 | 500  | 1.1904        | 0.6714                         |
| 0.1688 | 600  | 1.0998        | 0.6738                         |
| 0.1969 | 700  | 1.0655        | 0.6751                         |
| 0.2250 | 800  | 1.095         | 0.6781                         |
| 0.2532 | 900  | 1.1535        | 0.6813                         |
| 0.2813 | 1000 | 1.0047        | 0.6814                         |
| 0.3094 | 1100 | 1.0749        | 0.6809                         |
| 0.3376 | 1200 | 1.0642        | 0.6813                         |
| 0.3657 | 1300 | 1.0718        | 0.6851                         |
| 0.3938 | 1400 | 1.023         | 0.6854                         |
| 0.4219 | 1500 | 1.0429        | 0.6850                         |
| 0.4501 | 1600 | 1.0088        | 0.6849                         |
| 0.4782 | 1700 | 1.0129        | 0.6873                         |
| 0.5063 | 1800 | 0.988         | 0.6874                         |
| 0.5345 | 1900 | 1.0413        | 0.6882                         |
| 0.5626 | 2000 | 1.0043        | 0.6885                         |
| 0.5907 | 2100 | 0.9929        | 0.6886                         |
| 0.6188 | 2200 | 0.9403        | 0.6899                         |
| 0.6470 | 2300 | 0.9789        | 0.6907                         |
| 0.6751 | 2400 | 0.9595        | 0.6912                         |
| 0.7032 | 2500 | 0.9786        | 0.6914                         |
| 0.7314 | 2600 | 0.9647        | 0.6911                         |
| 0.7595 | 2700 | 0.9245        | 0.6897                         |
| 0.7876 | 2800 | 0.9685        | 0.6906                         |
| 0.8158 | 2900 | 0.9778        | 0.6896                         |
| 0.8439 | 3000 | 0.939         | 0.6906                         |
| 0.8720 | 3100 | 0.9822        | 0.6904                         |
| 0.9001 | 3200 | 1.0038        | 0.6913                         |
| 0.9283 | 3300 | 0.9297        | 0.6910                         |
| 0.9564 | 3400 | 0.9215        | 0.6915                         |
| 0.9845 | 3500 | 0.948         | 0.6919                         |


### Framework Versions
- Python: 3.11.4
- Sentence Transformers: 5.1.2
- Transformers: 4.57.3
- PyTorch: 2.9.1+cpu
- Accelerate: 1.12.0
- Datasets: 4.4.1
- Tokenizers: 0.22.1

## 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",
}
```

#### MatryoshkaLoss
```bibtex
@misc{kusupati2024matryoshka,
    title={Matryoshka Representation Learning},
    author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
    year={2024},
    eprint={2205.13147},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}
```

#### 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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