sentence-transformers safetensors bert sentence-similarity feature-extraction generated_from_trainer dataset_size:1830648 loss:AnglELoss en arxiv:1908.10084 arxiv:2309.12871 base_model:sentence-transformers/all-MiniLM-L6-v2 base_model:finetune:sentence-transformers/all-MiniLM-L6-v2 license:apache-2.0 autotrain_compatible text-embeddings-inference endpoints_compatible region:us
---
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:1830648
- loss:AnglELoss
widget:
- source_sentence: crunchy chips
  sentences:
  - big chips spiced gouda
  - purse
  - macaroni
- source_sentence: genuine leather luggage
  sentences:
  - janatte luggage
  - bomb chemise
  - purse
- source_sentence: head covers Rashguard
  sentences:
  - Double Shaded Blue Clutch
  - Rashguard
  - bathing costume
- source_sentence: hand Made Sweatpants
  sentences:
  - acid cleanser
  - reflective weave sweatpants
  - rashguard
- source_sentence: siamy wrap
  sentences:
  - siamy
  - hair revival
  - backpack
---

# all-MiniLM-L6-v5-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 fa97f6e7cb1a59073dff9e6b13e2715cf7475ac9 -->
- **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 = [
    'siamy wrap',
    'siamy',
    'hair revival',
]
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]
```

<!--
### Direct Usage (Transformers)

<details><summary>Click to see the direct usage in Transformers</summary>

</details>
-->

<!--
### Downstream Usage (Sentence Transformers)

You can finetune this model on your own dataset.

<details><summary>Click to expand</summary>

</details>
-->

<!--
### Out-of-Scope Use

*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->

<!--
## Bias, Risks and Limitations

*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->

<!--
### Recommendations

*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->

## 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 | loss   |
|:------:|:-----:|:-------------:|:------:|
| 0.0070 | 100   | 16.865        | -      |
| 0.0140 | 200   | 16.1556       | -      |
| 0.0210 | 300   | 14.8008       | -      |
| 0.0280 | 400   | 12.4025       | -      |
| 0.0350 | 500   | 9.7465        | -      |
| 0.0420 | 600   | 8.448         | -      |
| 0.0489 | 700   | 8.1951        | -      |
| 0.0559 | 800   | 8.1093        | -      |
| 0.0629 | 900   | 8.0567        | -      |
| 0.0699 | 1000  | 8.0401        | -      |
| 0.0769 | 1100  | 7.9491        | -      |
| 0.0839 | 1200  | 7.9494        | -      |
| 0.0909 | 1300  | 7.9386        | -      |
| 0.0979 | 1400  | 7.9033        | -      |
| 0.1049 | 1500  | 7.9055        | -      |
| 0.1119 | 1600  | 7.9203        | -      |
| 0.1189 | 1700  | 7.8381        | -      |
| 0.1259 | 1800  | 7.8679        | -      |
| 0.1328 | 1900  | 7.8686        | -      |
| 0.1398 | 2000  | 7.8252        | -      |
| 0.1468 | 2100  | 7.856         | -      |
| 0.1538 | 2200  | 7.8301        | -      |
| 0.1608 | 2300  | 7.8595        | -      |
| 0.1678 | 2400  | 7.8138        | -      |
| 0.1748 | 2500  | 7.812         | -      |
| 0.1818 | 2600  | 7.8261        | -      |
| 0.1888 | 2700  | 7.7988        | -      |
| 0.1958 | 2800  | 7.7965        | -      |
| 0.2028 | 2900  | 7.783         | -      |
| 0.2098 | 3000  | 7.7752        | -      |
| 0.2168 | 3100  | 7.7715        | -      |
| 0.2237 | 3200  | 7.7903        | -      |
| 0.2307 | 3300  | 7.7656        | -      |
| 0.2377 | 3400  | 7.749         | -      |
| 0.2447 | 3500  | 7.7662        | -      |
| 0.2517 | 3600  | 7.7492        | -      |
| 0.2587 | 3700  | 7.737         | -      |
| 0.2657 | 3800  | 7.7232        | -      |
| 0.2727 | 3900  | 7.7616        | -      |
| 0.2797 | 4000  | 7.7391        | -      |
| 0.2867 | 4100  | 7.7552        | -      |
| 0.2937 | 4200  | 7.7273        | -      |
| 0.3007 | 4300  | 7.7216        | -      |
| 0.3076 | 4400  | 7.7371        | -      |
| 0.3146 | 4500  | 7.7426        | -      |
| 0.3216 | 4600  | 7.7406        | -      |
| 0.3286 | 4700  | 7.712         | -      |
| 0.3356 | 4800  | 7.7466        | -      |
| 0.3426 | 4900  | 7.7058        | -      |
| 0.3496 | 5000  | 7.7139        | 7.6896 |
| 0.3566 | 5100  | 7.7457        | -      |
| 0.3636 | 5200  | 7.7172        | -      |
| 0.3706 | 5300  | 7.739         | -      |
| 0.3776 | 5400  | 7.7259        | -      |
| 0.3846 | 5500  | 7.6977        | -      |
| 0.3916 | 5600  | 7.7237        | -      |
| 0.3985 | 5700  | 7.7118        | -      |
| 0.4055 | 5800  | 7.7099        | -      |
| 0.4125 | 5900  | 7.7142        | -      |
| 0.4195 | 6000  | 7.6885        | -      |
| 0.4265 | 6100  | 7.6799        | -      |
| 0.4335 | 6200  | 7.7039        | -      |
| 0.4405 | 6300  | 7.6825        | -      |
| 0.4475 | 6400  | 7.6846        | -      |
| 0.4545 | 6500  | 7.7078        | -      |
| 0.4615 | 6600  | 7.6945        | -      |
| 0.4685 | 6700  | 7.7017        | -      |
| 0.4755 | 6800  | 7.6781        | -      |
| 0.4825 | 6900  | 7.6885        | -      |
| 0.4894 | 7000  | 7.7426        | -      |
| 0.4964 | 7100  | 7.6809        | -      |
| 0.5034 | 7200  | 7.6977        | -      |
| 0.5104 | 7300  | 7.6964        | -      |
| 0.5174 | 7400  | 7.6834        | -      |
| 0.5244 | 7500  | 7.6593        | -      |
| 0.5314 | 7600  | 7.6745        | -      |
| 0.5384 | 7700  | 7.6587        | -      |
| 0.5454 | 7800  | 7.6389        | -      |
| 0.5524 | 7900  | 7.6298        | -      |
| 0.5594 | 8000  | 7.6693        | -      |
| 0.5664 | 8100  | 7.6454        | -      |
| 0.5733 | 8200  | 7.6491        | -      |
| 0.5803 | 8300  | 7.661         | -      |
| 0.5873 | 8400  | 7.6525        | -      |
| 0.5943 | 8500  | 7.6669        | -      |
| 0.6013 | 8600  | 7.6379        | -      |
| 0.6083 | 8700  | 7.6706        | -      |
| 0.6153 | 8800  | 7.6487        | -      |
| 0.6223 | 8900  | 7.6607        | -      |
| 0.6293 | 9000  | 7.6334        | -      |
| 0.6363 | 9100  | 7.6891        | -      |
| 0.6433 | 9200  | 7.734         | -      |
| 0.6503 | 9300  | 7.6283        | -      |
| 0.6573 | 9400  | 7.6461        | -      |
| 0.6642 | 9500  | 7.623         | -      |
| 0.6712 | 9600  | 7.6251        | -      |
| 0.6782 | 9700  | 7.6663        | -      |
| 0.6852 | 9800  | 7.6376        | -      |
| 0.6922 | 9900  | 7.6834        | -      |
| 0.6992 | 10000 | 7.6851        | 7.6099 |
| 0.7062 | 10100 | 7.6034        | -      |
| 0.7132 | 10200 | 7.6512        | -      |
| 0.7202 | 10300 | 7.6413        | -      |
| 0.7272 | 10400 | 7.6083        | -      |
| 0.7342 | 10500 | 7.6475        | -      |
| 0.7412 | 10600 | 7.61          | -      |
| 0.7481 | 10700 | 7.6404        | -      |
| 0.7551 | 10800 | 7.6308        | -      |
| 0.7621 | 10900 | 7.638         | -      |
| 0.7691 | 11000 | 7.5954        | -      |
| 0.7761 | 11100 | 7.6037        | -      |
| 0.7831 | 11200 | 7.6405        | -      |
| 0.7901 | 11300 | 7.6396        | -      |
| 0.7971 | 11400 | 7.5898        | -      |
| 0.8041 | 11500 | 7.644         | -      |
| 0.8111 | 11600 | 7.639         | -      |
| 0.8181 | 11700 | 7.6146        | -      |
| 0.8251 | 11800 | 7.6076        | -      |
| 0.8321 | 11900 | 7.5997        | -      |
| 0.8390 | 12000 | 7.6196        | -      |
| 0.8460 | 12100 | 7.6139        | -      |
| 0.8530 | 12200 | 7.6335        | -      |
| 0.8600 | 12300 | 7.6057        | -      |
| 0.8670 | 12400 | 7.5759        | -      |
| 0.8740 | 12500 | 7.6044        | -      |
| 0.8810 | 12600 | 7.589         | -      |
| 0.8880 | 12700 | 7.5871        | -      |
| 0.8950 | 12800 | 7.6161        | -      |
| 0.9020 | 12900 | 7.5797        | -      |
| 0.9090 | 13000 | 7.6202        | -      |
| 0.9160 | 13100 | 7.6116        | -      |
| 0.9229 | 13200 | 7.6253        | -      |
| 0.9299 | 13300 | 7.5891        | -      |
| 0.9369 | 13400 | 7.5856        | -      |
| 0.9439 | 13500 | 7.5824        | -      |
| 0.9509 | 13600 | 7.6288        | -      |
| 0.9579 | 13700 | 7.5653        | -      |
| 0.9649 | 13800 | 7.6073        | -      |
| 0.9719 | 13900 | 7.5958        | -      |
| 0.9789 | 14000 | 7.599         | -      |
| 0.9859 | 14100 | 7.5982        | -      |
| 0.9929 | 14200 | 7.5634        | -      |
| 0.9999 | 14300 | 7.5923        | -      |
| 1.0069 | 14400 | 7.6072        | -      |
| 1.0138 | 14500 | 7.5589        | -      |
| 1.0208 | 14600 | 7.6           | -      |
| 1.0278 | 14700 | 7.5464        | -      |
| 1.0348 | 14800 | 7.5824        | -      |
| 1.0418 | 14900 | 7.5528        | -      |
| 1.0488 | 15000 | 7.568         | 7.5618 |
| 1.0558 | 15100 | 7.559         | -      |
| 1.0628 | 15200 | 7.5555        | -      |
| 1.0698 | 15300 | 7.552         | -      |
| 1.0768 | 15400 | 7.5851        | -      |
| 1.0838 | 15500 | 7.5256        | -      |
| 1.0908 | 15600 | 7.5683        | -      |
| 1.0977 | 15700 | 7.5909        | -      |
| 1.1047 | 15800 | 7.5655        | -      |
| 1.1117 | 15900 | 7.5476        | -      |
| 1.1187 | 16000 | 7.5721        | -      |
| 1.1257 | 16100 | 7.5593        | -      |
| 1.1327 | 16200 | 7.5783        | -      |
| 1.1397 | 16300 | 7.5905        | -      |
| 1.1467 | 16400 | 7.542         | -      |
| 1.1537 | 16500 | 7.5794        | -      |
| 1.1607 | 16600 | 7.5669        | -      |
| 1.1677 | 16700 | 7.5738        | -      |
| 1.1747 | 16800 | 7.5431        | -      |
| 1.1817 | 16900 | 7.5401        | -      |
| 1.1886 | 17000 | 7.5629        | -      |
| 1.1956 | 17100 | 7.5534        | -      |
| 1.2026 | 17200 | 7.571         | -      |
| 1.2096 | 17300 | 7.5387        | -      |
| 1.2166 | 17400 | 7.5596        | -      |
| 1.2236 | 17500 | 7.5427        | -      |
| 1.2306 | 17600 | 7.5305        | -      |
| 1.2376 | 17700 | 7.556         | -      |
| 1.2446 | 17800 | 7.5442        | -      |
| 1.2516 | 17900 | 7.5635        | -      |
| 1.2586 | 18000 | 7.5675        | -      |
| 1.2656 | 18100 | 7.5412        | -      |
| 1.2725 | 18200 | 7.5148        | -      |
| 1.2795 | 18300 | 7.546         | -      |
| 1.2865 | 18400 | 7.5608        | -      |
| 1.2935 | 18500 | 7.5269        | -      |
| 1.3005 | 18600 | 7.5614        | -      |
| 1.3075 | 18700 | 7.5276        | -      |
| 1.3145 | 18800 | 7.5586        | -      |
| 1.3215 | 18900 | 7.5783        | -      |
| 1.3285 | 19000 | 7.5312        | -      |
| 1.3355 | 19100 | 7.536         | -      |
| 1.3425 | 19200 | 7.5497        | -      |
| 1.3495 | 19300 | 7.5256        | -      |
| 1.3565 | 19400 | 7.5364        | -      |
| 1.3634 | 19500 | 7.5311        | -      |
| 1.3704 | 19600 | 7.5557        | -      |
| 1.3774 | 19700 | 7.5531        | -      |
| 1.3844 | 19800 | 7.4844        | -      |
| 1.3914 | 19900 | 7.5408        | -      |
| 1.3984 | 20000 | 7.5336        | 7.5100 |
| 1.4054 | 20100 | 7.524         | -      |
| 1.4124 | 20200 | 7.5236        | -      |
| 1.4194 | 20300 | 7.5354        | -      |
| 1.4264 | 20400 | 7.5228        | -      |
| 1.4334 | 20500 | 7.5453        | -      |
| 1.4404 | 20600 | 7.5492        | -      |
| 1.4474 | 20700 | 7.5145        | -      |
| 1.4543 | 20800 | 7.5464        | -      |
| 1.4613 | 20900 | 7.5377        | -      |
| 1.4683 | 21000 | 7.5221        | -      |
| 1.4753 | 21100 | 7.5793        | -      |
| 1.4823 | 21200 | 7.5234        | -      |
| 1.4893 | 21300 | 7.4922        | -      |
| 1.4963 | 21400 | 7.5196        | -      |
| 1.5033 | 21500 | 7.5265        | -      |
| 1.5103 | 21600 | 7.5251        | -      |
| 1.5173 | 21700 | 7.4982        | -      |
| 1.5243 | 21800 | 7.5225        | -      |
| 1.5313 | 21900 | 7.5725        | -      |
| 1.5382 | 22000 | 7.5109        | -      |
| 1.5452 | 22100 | 7.5382        | -      |
| 1.5522 | 22200 | 7.5131        | -      |
| 1.5592 | 22300 | 7.5376        | -      |
| 1.5662 | 22400 | 7.5373        | -      |
| 1.5732 | 22500 | 7.5467        | -      |
| 1.5802 | 22600 | 7.519         | -      |
| 1.5872 | 22700 | 7.5137        | -      |
| 1.5942 | 22800 | 7.5511        | -      |
| 1.6012 | 22900 | 7.5376        | -      |
| 1.6082 | 23000 | 7.4912        | -      |
| 1.6152 | 23100 | 7.5331        | -      |
| 1.6222 | 23200 | 7.5223        | -      |
| 1.6291 | 23300 | 7.5342        | -      |
| 1.6361 | 23400 | 7.5171        | -      |
| 1.6431 | 23500 | 7.5225        | -      |
| 1.6501 | 23600 | 7.5273        | -      |
| 1.6571 | 23700 | 7.5179        | -      |
| 1.6641 | 23800 | 7.5508        | -      |
| 1.6711 | 23900 | 7.5064        | -      |
| 1.6781 | 24000 | 7.5008        | -      |
| 1.6851 | 24100 | 7.5046        | -      |
| 1.6921 | 24200 | 7.5295        | -      |
| 1.6991 | 24300 | 7.4983        | -      |
| 1.7061 | 24400 | 7.5451        | -      |
| 1.7130 | 24500 | 7.5092        | -      |
| 1.7200 | 24600 | 7.5434        | -      |
| 1.7270 | 24700 | 7.5296        | -      |
| 1.7340 | 24800 | 7.5036        | -      |
| 1.7410 | 24900 | 7.5133        | -      |
| 1.7480 | 25000 | 7.5222        | 7.4986 |
| 1.7550 | 25100 | 7.4928        | -      |
| 1.7620 | 25200 | 7.5338        | -      |
| 1.7690 | 25300 | 7.4937        | -      |
| 1.7760 | 25400 | 7.542         | -      |
| 1.7830 | 25500 | 7.5296        | -      |
| 1.7900 | 25600 | 7.5069        | -      |
| 1.7970 | 25700 | 7.5311        | -      |
| 1.8039 | 25800 | 7.5433        | -      |
| 1.8109 | 25900 | 7.4879        | -      |
| 1.8179 | 26000 | 7.5547        | -      |
| 1.8249 | 26100 | 7.5497        | -      |
| 1.8319 | 26200 | 7.5156        | -      |
| 1.8389 | 26300 | 7.4743        | -      |
| 1.8459 | 26400 | 7.4897        | -      |
| 1.8529 | 26500 | 7.4995        | -      |
| 1.8599 | 26600 | 7.5073        | -      |
| 1.8669 | 26700 | 7.5027        | -      |
| 1.8739 | 26800 | 7.4696        | -      |
| 1.8809 | 26900 | 7.4862        | -      |
| 1.8878 | 27000 | 7.5107        | -      |
| 1.8948 | 27100 | 7.4674        | -      |
| 1.9018 | 27200 | 7.4839        | -      |
| 1.9088 | 27300 | 7.5098        | -      |
| 1.9158 | 27400 | 7.5152        | -      |
| 1.9228 | 27500 | 7.5148        | -      |
| 1.9298 | 27600 | 7.4856        | -      |
| 1.9368 | 27700 | 7.5305        | -      |
| 1.9438 | 27800 | 7.4926        | -      |
| 1.9508 | 27900 | 7.5261        | -      |
| 1.9578 | 28000 | 7.5152        | -      |
| 1.9648 | 28100 | 7.5328        | -      |
| 1.9718 | 28200 | 7.4807        | -      |
| 1.9787 | 28300 | 7.4699        | -      |
| 1.9857 | 28400 | 7.5067        | -      |
| 1.9927 | 28500 | 7.4996        | -      |
| 1.9997 | 28600 | 7.5166        | -      |

</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",
}
```

#### AnglELoss
```bibtex
@misc{li2023angleoptimized,
    title={AnglE-optimized Text Embeddings},
    author={Xianming Li and Jing Li},
    year={2023},
    eprint={2309.12871},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}
```

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