---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:78879
- loss:CosineSimilarityLoss
base_model: intfloat/multilingual-e5-base
widget:
- source_sentence: Somatotropin Ab
  sentences:
  - Desethylamiodarone
  - Glucose^7H post XXX challenge
  - Somatotropin Ab
- source_sentence: Erythrocytes.fetal/1000 erythrocytes
  sentences:
  - levoFLOXacin
  - Pathologist interpretation
  - Pepsinogen I
- source_sentence: Aggregazione piastrinica.arachidonato indotta
  sentences:
  - Epidermal growth factor
  - Bilirubin.glucuronidated/Bilirubin.total
  - Platelet aggregation.arachidonate induced
- source_sentence: Parathormoon.intact^5 min na uitsnijding  in serum of plasma
  sentences:
  - Fatty acids.very long chain
  - Estradiol^4th specimen post XXX challenge
  - Parathyrin.intact^5M post excision
- source_sentence: Karboksühemoglobiin/hemoglobiin.üld
  sentences:
  - Ammonia
  - Carboxyhemoglobin/Hemoglobin.total
  - Procainamide+N-acetylprocainamide
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")
# Run inference
sentences = [
    'Karboksühemoglobiin/hemoglobiin.üld',
    'Carboxyhemoglobin/Hemoglobin.total',
    'Procainamide+N-acetylprocainamide',
]
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: 78,879 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: 11.64 tokens</li><li>max: 36 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 10.26 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.59</li><li>max: 1.0</li></ul> |
* Samples:
  | sentence_0                                        | sentence_1                                       | label            |
  |:--------------------------------------------------|:-------------------------------------------------|:-----------------|
  | <code>Rakud.CD3+HLA-DR+/100 raku kohta</code>     | <code>Cells.CD3+HLA-DR+/100 cells</code>         | <code>1.0</code> |
  | <code>Zellen.FMC7/100 Zellen</code>               | <code>Cells.FMC7/100 cells</code>                | <code>1.0</code> |
  | <code>Apolipoprotéine AI/apolipoprotéine B</code> | <code>Apolipoprotein A-I/Apolipoprotein B</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`: 10
- `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`: 10
- `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 |
|:------:|:-----:|:-------------:|
| 0.1014 | 500   | 0.0633        |
| 0.2028 | 1000  | 0.0332        |
| 0.3043 | 1500  | 0.0296        |
| 0.4057 | 2000  | 0.0266        |
| 0.5071 | 2500  | 0.024         |
| 0.6085 | 3000  | 0.0239        |
| 0.7099 | 3500  | 0.0216        |
| 0.8114 | 4000  | 0.0205        |
| 0.9128 | 4500  | 0.0187        |
| 1.0142 | 5000  | 0.0185        |
| 1.1156 | 5500  | 0.0149        |
| 1.2170 | 6000  | 0.015         |
| 1.3185 | 6500  | 0.0142        |
| 1.4199 | 7000  | 0.0152        |
| 1.5213 | 7500  | 0.0138        |
| 1.6227 | 8000  | 0.0131        |
| 1.7241 | 8500  | 0.014         |
| 1.8256 | 9000  | 0.0133        |
| 1.9270 | 9500  | 0.0125        |
| 2.0284 | 10000 | 0.0128        |
| 2.1298 | 10500 | 0.0093        |
| 2.2312 | 11000 | 0.0091        |
| 2.3327 | 11500 | 0.0097        |
| 2.4341 | 12000 | 0.0096        |
| 2.5355 | 12500 | 0.0097        |
| 2.6369 | 13000 | 0.0093        |
| 2.7383 | 13500 | 0.0099        |
| 2.8398 | 14000 | 0.0104        |
| 2.9412 | 14500 | 0.009         |
| 3.0426 | 15000 | 0.0084        |
| 3.1440 | 15500 | 0.0065        |
| 3.2454 | 16000 | 0.0062        |
| 3.3469 | 16500 | 0.0062        |
| 3.4483 | 17000 | 0.0068        |
| 3.5497 | 17500 | 0.0076        |
| 3.6511 | 18000 | 0.0078        |
| 3.7525 | 18500 | 0.0068        |
| 3.8540 | 19000 | 0.008         |
| 3.9554 | 19500 | 0.0076        |
| 4.0568 | 20000 | 0.0057        |
| 4.1582 | 20500 | 0.0054        |
| 4.2596 | 21000 | 0.0052        |
| 4.3611 | 21500 | 0.0052        |
| 4.4625 | 22000 | 0.0056        |
| 4.5639 | 22500 | 0.0055        |
| 4.6653 | 23000 | 0.0057        |
| 4.7667 | 23500 | 0.006         |
| 4.8682 | 24000 | 0.0054        |
| 4.9696 | 24500 | 0.0052        |
| 5.0710 | 25000 | 0.0045        |
| 5.1724 | 25500 | 0.0039        |
| 5.2738 | 26000 | 0.0043        |
| 5.3753 | 26500 | 0.004         |
| 5.4767 | 27000 | 0.0044        |
| 5.5781 | 27500 | 0.0045        |
| 5.6795 | 28000 | 0.0039        |
| 5.7809 | 28500 | 0.0043        |
| 5.8824 | 29000 | 0.0047        |
| 5.9838 | 29500 | 0.0049        |
| 6.0852 | 30000 | 0.003         |
| 6.1866 | 30500 | 0.0034        |
| 6.2880 | 31000 | 0.003         |
| 6.3895 | 31500 | 0.0031        |
| 6.4909 | 32000 | 0.0033        |
| 6.5923 | 32500 | 0.0035        |
| 6.6937 | 33000 | 0.0037        |
| 6.7951 | 33500 | 0.0039        |
| 6.8966 | 34000 | 0.004         |
| 6.9980 | 34500 | 0.003         |
| 7.0994 | 35000 | 0.0024        |
| 7.2008 | 35500 | 0.0026        |
| 7.3022 | 36000 | 0.0029        |
| 7.4037 | 36500 | 0.0029        |
| 7.5051 | 37000 | 0.0025        |
| 7.6065 | 37500 | 0.0026        |
| 7.7079 | 38000 | 0.0032        |
| 7.8093 | 38500 | 0.0032        |
| 7.9108 | 39000 | 0.0029        |
| 8.0122 | 39500 | 0.0028        |
| 8.1136 | 40000 | 0.0024        |
| 8.2150 | 40500 | 0.0021        |
| 8.3164 | 41000 | 0.0022        |
| 8.4178 | 41500 | 0.0022        |
| 8.5193 | 42000 | 0.0024        |
| 8.6207 | 42500 | 0.0025        |
| 8.7221 | 43000 | 0.0023        |
| 8.8235 | 43500 | 0.0021        |
| 8.9249 | 44000 | 0.0026        |
| 9.0264 | 44500 | 0.0025        |
| 9.1278 | 45000 | 0.0021        |
| 9.2292 | 45500 | 0.0017        |
| 9.3306 | 46000 | 0.0022        |
| 9.4320 | 46500 | 0.002         |
| 9.5335 | 47000 | 0.0021        |
| 9.6349 | 47500 | 0.0019        |
| 9.7363 | 48000 | 0.0021        |
| 9.8377 | 48500 | 0.002         |
| 9.9391 | 49000 | 0.0021        |


### 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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