---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:109998
- loss:CosineSimilarityLoss
base_model: intfloat/multilingual-e5-base
widget:
- source_sentence: hepatitis-c-virus ak
  sentences:
  - cells.cd8+cd38+
  - hepatitis c virus ab
  - a2 ab
- source_sentence: lymphs smig/lymph nfr bld
  sentences:
  - methionin
  - alkaline phosphatase.liver/alkaline phosphatase.total
  - lymphocytes.smig/100 lymphocytes
- source_sentence: trimipramine
  sentences:
  - parathyrin.mid molecule
  - phenylketones
  - trimeprazine
- source_sentence: dacriocitos
  sentences:
  - sample icteric
  - fentioon
  - dacryocytes
- source_sentence: homo-gamma linolenate
  sentences:
  - platine
  - complement factor b
  - alpha linolenate
pipeline_tag: sentence-similarity
library_name: sentence-transformers
---

# SentenceTransformer based on intfloat/multilingual-e5-base

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [intfloat/multilingual-e5-base](https://huggingface.co/intfloat/multilingual-e5-base). 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:** [intfloat/multilingual-e5-base](https://huggingface.co/intfloat/multilingual-e5-base) <!-- at revision d13f1b27baf31030b7fd040960d60d909913633f -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **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: XLMRobertaModel 
  (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})
  (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("iddqd21/fine-tuned-e5-semantic-similarity_lowercase")
# Run inference
sentences = [
    'homo-gamma linolenate',
    'alpha linolenate',
    'complement factor b',
]
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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## Training Details

### Training Dataset

#### Unnamed Dataset


* Size: 109,998 training samples
* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence_0                                                                       | sentence_1                                                                       | label                                                          |
  |:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------|
  | type    | string                                                                           | string                                                                           | float                                                          |
  | details | <ul><li>min: 3 tokens</li><li>mean: 10.8 tokens</li><li>max: 33 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 9.36 tokens</li><li>max: 27 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.44</li><li>max: 1.0</li></ul> |
* Samples:
  | sentence_0                                                    | sentence_1                                                            | label            |
  |:--------------------------------------------------------------|:----------------------------------------------------------------------|:-----------------|
  | <code>雄烯二酮</code>                                             | <code>androstenedione</code>                                          | <code>1.0</code> |
  | <code>follitropin^30dk gonadotropin salıcı hormon dozu</code> | <code>follitropin^30m post dose gonadotropin releasing hormone</code> | <code>1.0</code> |
  | <code>ch50 serpl-mcnc</code>                                  | <code>complement total hemolytic ch50</code>                          | <code>1.0</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

- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `num_train_epochs`: 5
- `multi_dataset_batch_sampler`: round_robin

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

- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: no
- `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
- `num_train_epochs`: 5
- `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
- `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`: round_robin

</details>

### Training Logs
| Epoch  | Step  | Training Loss |
|:------:|:-----:|:-------------:|
| 0.0727 | 500   | 0.1541        |
| 0.1455 | 1000  | 0.1012        |
| 0.2182 | 1500  | 0.0972        |
| 0.2909 | 2000  | 0.0862        |
| 0.3636 | 2500  | 0.0838        |
| 0.4364 | 3000  | 0.0818        |
| 0.5091 | 3500  | 0.0804        |
| 0.5818 | 4000  | 0.0746        |
| 0.6545 | 4500  | 0.0726        |
| 0.7273 | 5000  | 0.0693        |
| 0.8    | 5500  | 0.0688        |
| 0.8727 | 6000  | 0.0696        |
| 0.9455 | 6500  | 0.0661        |
| 1.0182 | 7000  | 0.0619        |
| 1.0909 | 7500  | 0.0537        |
| 1.1636 | 8000  | 0.0536        |
| 1.2364 | 8500  | 0.0537        |
| 1.3091 | 9000  | 0.0548        |
| 1.3818 | 9500  | 0.0526        |
| 1.4545 | 10000 | 0.0526        |
| 1.5273 | 10500 | 0.0489        |
| 1.6    | 11000 | 0.0501        |
| 1.6727 | 11500 | 0.0498        |
| 1.7455 | 12000 | 0.0487        |
| 1.8182 | 12500 | 0.0454        |
| 1.8909 | 13000 | 0.0454        |
| 1.9636 | 13500 | 0.0455        |
| 2.0364 | 14000 | 0.0426        |
| 2.1091 | 14500 | 0.0382        |
| 2.1818 | 15000 | 0.039         |
| 2.2545 | 15500 | 0.0368        |
| 2.3273 | 16000 | 0.0397        |
| 2.4    | 16500 | 0.0355        |
| 2.4727 | 17000 | 0.035         |
| 2.5455 | 17500 | 0.036         |
| 2.6182 | 18000 | 0.0348        |
| 2.6909 | 18500 | 0.0384        |
| 2.7636 | 19000 | 0.0338        |
| 2.8364 | 19500 | 0.0331        |
| 2.9091 | 20000 | 0.0345        |
| 2.9818 | 20500 | 0.0337        |
| 3.0545 | 21000 | 0.0293        |
| 3.1273 | 21500 | 0.0274        |
| 3.2    | 22000 | 0.0284        |
| 3.2727 | 22500 | 0.028         |
| 3.3455 | 23000 | 0.0275        |
| 3.4182 | 23500 | 0.03          |
| 3.4909 | 24000 | 0.027         |
| 3.5636 | 24500 | 0.0279        |
| 3.6364 | 25000 | 0.0285        |
| 3.7091 | 25500 | 0.0288        |
| 3.7818 | 26000 | 0.0263        |
| 3.8545 | 26500 | 0.0289        |
| 3.9273 | 27000 | 0.0271        |
| 4.0    | 27500 | 0.0268        |
| 4.0727 | 28000 | 0.0234        |
| 4.1455 | 28500 | 0.0228        |
| 4.2182 | 29000 | 0.0235        |
| 4.2909 | 29500 | 0.024         |
| 4.3636 | 30000 | 0.0234        |
| 4.4364 | 30500 | 0.0235        |
| 4.5091 | 31000 | 0.0233        |
| 4.5818 | 31500 | 0.0239        |
| 4.6545 | 32000 | 0.0228        |
| 4.7273 | 32500 | 0.0236        |
| 4.8    | 33000 | 0.0236        |
| 4.8727 | 33500 | 0.0223        |
| 4.9455 | 34000 | 0.022         |


### Framework Versions
- Python: 3.9.20
- Sentence Transformers: 3.3.1
- Transformers: 4.47.1
- PyTorch: 2.5.1+rocm6.2
- Accelerate: 1.2.1
- Datasets: 3.2.0
- 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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