---
base_model: sentence-transformers/all-MiniLM-L6-v2
language:
- en
library_name: sentence-transformers
license: apache-2.0
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:1048433
- loss:CoSENTLoss
widget:
- source_sentence: specialty supplement
  sentences:
  - serving spoon
  - water filter
  - furniture accessory
- source_sentence: pediatrics medicine
  sentences:
  - flour
  - playstation accessory
  - pantry
- source_sentence: hook
  sentences:
  - serving dish
  - serving fork
  - floor game
- source_sentence: pasta
  sentences:
  - neckwear and scarf
  - frying basket
  - chocolate
- source_sentence: electronic instrument
  sentences:
  - Salad
  - sirlion
  - gardening accessory
---

# all-MiniLM-L6-v17-pair_score

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). 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 tokens
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
- **Language:** en
- **License:** apache-2.0

### 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': 256, 'do_lower_case': False}) with Transformer model: 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("sentence_transformers_model_id")
# Run inference
sentences = [
    'electronic instrument',
    'sirlion',
    'Salad',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

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

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## Training Details

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

- `eval_strategy`: steps
- `per_device_train_batch_size`: 128
- `per_device_eval_batch_size`: 128
- `learning_rate`: 2e-05
- `num_train_epochs`: 2
- `warmup_ratio`: 0.1
- `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`: 128
- `per_device_eval_batch_size`: 128
- `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`: 2
- `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`: 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`: 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
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
<details><summary>Click to expand</summary>

| Epoch  | Step  | Training Loss |
|:------:|:-----:|:-------------:|
| 0.0122 | 100   | 10.562        |
| 0.0244 | 200   | 10.0184       |
| 0.0366 | 300   | 9.398         |
| 0.0488 | 400   | 8.8197        |
| 0.0610 | 500   | 8.3899        |
| 0.0733 | 600   | 7.8989        |
| 0.0855 | 700   | 7.6515        |
| 0.0977 | 800   | 7.3998        |
| 0.1099 | 900   | 7.166         |
| 0.1221 | 1000  | 6.9383        |
| 0.1343 | 1100  | 6.6043        |
| 0.1465 | 1200  | 6.3584        |
| 0.1587 | 1300  | 6.0252        |
| 0.1709 | 1400  | 5.7639        |
| 0.1831 | 1500  | 5.6496        |
| 0.1953 | 1600  | 5.2169        |
| 0.2075 | 1700  | 5.1389        |
| 0.2198 | 1800  | 4.9316        |
| 0.2320 | 1900  | 4.8547        |
| 0.2442 | 2000  | 4.6022        |
| 0.2564 | 2100  | 4.7122        |
| 0.2686 | 2200  | 4.5965        |
| 0.2808 | 2300  | 3.9285        |
| 0.2930 | 2400  | 4.0168        |
| 0.3052 | 2500  | 4.2677        |
| 0.3174 | 2600  | 4.147         |
| 0.3296 | 2700  | 4.101         |
| 0.3418 | 2800  | 3.8629        |
| 0.3540 | 2900  | 3.86          |
| 0.3663 | 3000  | 3.5607        |
| 0.3785 | 3100  | 3.8495        |
| 0.3907 | 3200  | 3.5558        |
| 0.4029 | 3300  | 3.7251        |
| 0.4151 | 3400  | 3.5233        |
| 0.4273 | 3500  | 3.8677        |
| 0.4395 | 3600  | 3.3688        |
| 0.4517 | 3700  | 3.479         |
| 0.4639 | 3800  | 3.1691        |
| 0.4761 | 3900  | 3.1791        |
| 0.4883 | 4000  | 3.2925        |
| 0.5005 | 4100  | 2.6573        |
| 0.5128 | 4200  | 2.8804        |
| 0.5250 | 4300  | 3.0418        |
| 0.5372 | 4400  | 2.7162        |
| 0.5494 | 4500  | 2.8449        |
| 0.5616 | 4600  | 2.7159        |
| 0.5738 | 4700  | 2.5733        |
| 0.5860 | 4800  | 2.5866        |
| 0.5982 | 4900  | 2.9195        |
| 0.6104 | 5000  | 2.0384        |
| 0.6226 | 5100  | 2.6745        |
| 0.6348 | 5200  | 2.3901        |
| 0.6471 | 5300  | 2.2872        |
| 0.6593 | 5400  | 2.0086        |
| 0.6715 | 5500  | 2.198         |
| 0.6837 | 5600  | 1.9139        |
| 0.6959 | 5700  | 2.0432        |
| 0.7081 | 5800  | 2.1445        |
| 0.7203 | 5900  | 2.5626        |
| 0.7325 | 6000  | 2.1707        |
| 0.7447 | 6100  | 2.1568        |
| 0.7569 | 6200  | 2.0102        |
| 0.7691 | 6300  | 2.0012        |
| 0.7813 | 6400  | 1.8381        |
| 0.7936 | 6500  | 1.7552        |
| 0.8058 | 6600  | 1.9704        |
| 0.8180 | 6700  | 1.6397        |
| 0.8302 | 6800  | 1.8857        |
| 0.8424 | 6900  | 1.8036        |
| 0.8546 | 7000  | 1.721         |
| 0.8668 | 7100  | 1.6888        |
| 0.8790 | 7200  | 1.7908        |
| 0.8912 | 7300  | 1.5851        |
| 0.9034 | 7400  | 1.7986        |
| 0.9156 | 7500  | 1.2549        |
| 0.9278 | 7600  | 1.5765        |
| 0.9401 | 7700  | 1.4524        |
| 0.9523 | 7800  | 1.2767        |
| 0.9645 | 7900  | 1.1604        |
| 0.9767 | 8000  | 1.557         |
| 0.9889 | 8100  | 1.1124        |
| 1.0011 | 8200  | 1.3092        |
| 1.0133 | 8300  | 1.598         |
| 1.0255 | 8400  | 1.6242        |
| 1.0377 | 8500  | 1.4893        |
| 1.0499 | 8600  | 1.0693        |
| 1.0621 | 8700  | 0.9369        |
| 1.0743 | 8800  | 1.1275        |
| 1.0866 | 8900  | 1.3307        |
| 1.0988 | 9000  | 1.0498        |
| 1.1110 | 9100  | 1.2496        |
| 1.1232 | 9200  | 1.1011        |
| 1.1354 | 9300  | 1.0483        |
| 1.1476 | 9400  | 1.2593        |
| 1.1598 | 9500  | 0.9409        |
| 1.1720 | 9600  | 1.0609        |
| 1.1842 | 9700  | 1.1829        |
| 1.1964 | 9800  | 1.0511        |
| 1.2086 | 9900  | 0.919         |
| 1.2209 | 10000 | 0.9473        |
| 1.2331 | 10100 | 1.2604        |
| 1.2453 | 10200 | 1.17          |
| 1.2575 | 10300 | 1.181         |
| 1.2697 | 10400 | 0.9092        |
| 1.2819 | 10500 | 0.9655        |
| 1.2941 | 10600 | 1.058         |
| 1.3063 | 10700 | 1.283         |
| 1.3185 | 10800 | 1.1552        |
| 1.3307 | 10900 | 0.858         |
| 1.3429 | 11000 | 0.8581        |
| 1.3551 | 11100 | 1.1272        |
| 1.3674 | 11200 | 1.0127        |
| 1.3796 | 11300 | 0.7372        |
| 1.3918 | 11400 | 0.913         |
| 1.4040 | 11500 | 0.8728        |
| 1.4162 | 11600 | 1.1358        |
| 1.4284 | 11700 | 0.9387        |
| 1.4406 | 11800 | 0.8424        |
| 1.4528 | 11900 | 0.8999        |
| 1.4650 | 12000 | 1.2505        |
| 1.4772 | 12100 | 1.0151        |
| 1.4894 | 12200 | 0.8013        |
| 1.5016 | 12300 | 1.1422        |
| 1.5139 | 12400 | 1.1518        |
| 1.5261 | 12500 | 1.0553        |
| 1.5383 | 12600 | 0.9228        |
| 1.5505 | 12700 | 1.2036        |
| 1.5627 | 12800 | 1.1064        |
| 1.5749 | 12900 | 0.7599        |
| 1.5871 | 13000 | 0.6376        |
| 1.5993 | 13100 | 1.002         |
| 1.6115 | 13200 | 0.9072        |
| 1.6237 | 13300 | 0.9645        |
| 1.6359 | 13400 | 0.9208        |
| 1.6482 | 13500 | 1.1439        |
| 1.6604 | 13600 | 1.3721        |
| 1.6726 | 13700 | 0.8702        |
| 1.6848 | 13800 | 0.9476        |
| 1.6970 | 13900 | 1.1247        |
| 1.7092 | 14000 | 1.1059        |
| 1.7214 | 14100 | 0.9272        |
| 1.7336 | 14200 | 0.8893        |
| 1.7458 | 14300 | 0.6242        |
| 1.7580 | 14400 | 0.6779        |
| 1.7702 | 14500 | 0.7436        |
| 1.7824 | 14600 | 0.7655        |
| 1.7947 | 14700 | 0.7952        |
| 1.8069 | 14800 | 1.1916        |
| 1.8191 | 14900 | 0.7219        |
| 1.8313 | 15000 | 0.7313        |
| 1.8435 | 15100 | 0.8224        |
| 1.8557 | 15200 | 0.8756        |
| 1.8679 | 15300 | 0.622         |
| 1.8801 | 15400 | 1.0309        |
| 1.8923 | 15500 | 0.7322        |
| 1.9045 | 15600 | 0.9327        |
| 1.9167 | 15700 | 0.8632        |
| 1.9289 | 15800 | 1.0087        |
| 1.9412 | 15900 | 0.6738        |
| 1.9534 | 16000 | 0.8936        |
| 1.9656 | 16100 | 0.8083        |
| 1.9778 | 16200 | 0.7114        |
| 1.9900 | 16300 | 0.9119        |

</details>

### Framework Versions
- Python: 3.8.10
- Sentence Transformers: 3.1.1
- Transformers: 4.45.2
- PyTorch: 2.4.1+cu118
- Accelerate: 1.0.1
- Datasets: 3.0.1
- Tokenizers: 0.20.3

## 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",
}
```

#### CoSENTLoss
```bibtex
@online{kexuefm-8847,
    title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT},
    author={Su Jianlin},
    year={2022},
    month={Jan},
    url={https://kexue.fm/archives/8847},
}
```

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