transformers pytorch bert fill-mask clinical notes healthcare medical pharma base_model:emilyalsentzer/Bio_ClinicalBERT base_model:finetune:emilyalsentzer/Bio_ClinicalBERT license:mit autotrain_compatible endpoints_compatible region:us
---
base_model: emilyalsentzer/Bio_ClinicalBERT
license: mit
tags:
- clinical notes
- healthcare
- medical
- pharma
widget:
- text: A 25 year old woman with no history of interest, who is studied for presenting
    a history of [MASK] pain of predominance in right hypochondrium
model-index:
- name: clinical_bert
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# clinical_bert

This model is a fine-tuned version of [emilyalsentzer/Bio_ClinicalBERT](https://huggingface.co/emilyalsentzer/Bio_ClinicalBERT) on [PlanTL-GOB-ES/pharmaconer](https://huggingface.co/datasets/PlanTL-GOB-ES/pharmaconer).
It achieves the following results on the evaluation and test set:
- Validation Loss: 1.6020
- Test Loss: 1.6591

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- lr_scheduler_warmup_steps: 100
- training_steps: 5000

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log        | 0.78  | 100  | 1.9485          |
| No log        | 1.56  | 200  | 1.8681          |
| No log        | 2.34  | 300  | 1.8152          |
| No log        | 3.12  | 400  | 1.7886          |
| 1.9285        | 3.91  | 500  | 1.7309          |
| 1.9285        | 4.69  | 600  | 1.6810          |
| 1.9285        | 5.47  | 700  | 1.7065          |
| 1.9285        | 6.25  | 800  | 1.7067          |
| 1.9285        | 7.03  | 900  | 1.7312          |
| 1.6644        | 7.81  | 1000 | 1.7006          |
| 1.6644        | 8.59  | 1100 | 1.6736          |
| 1.6644        | 9.38  | 1200 | 1.6846          |
| 1.6644        | 10.16 | 1300 | 1.6621          |
| 1.6644        | 10.94 | 1400 | 1.6381          |
| 1.5247        | 11.72 | 1500 | 1.6281          |
| 1.5247        | 12.5  | 1600 | 1.6605          |
| 1.5247        | 13.28 | 1700 | 1.6770          |
| 1.5247        | 14.06 | 1800 | 1.6666          |
| 1.5247        | 14.84 | 1900 | 1.6620          |
| 1.4334        | 15.62 | 2000 | 1.6677          |
| 1.4334        | 16.41 | 2100 | 1.6311          |
| 1.4334        | 17.19 | 2200 | 1.6743          |
| 1.4334        | 17.97 | 2300 | 1.6586          |
| 1.4334        | 18.75 | 2400 | 1.6086          |
| 1.3423        | 19.53 | 2500 | 1.6229          |
| 1.3423        | 20.31 | 2600 | 1.6475          |
| 1.3423        | 21.09 | 2700 | 1.6388          |
| 1.3423        | 21.88 | 2800 | 1.6275          |
| 1.3423        | 22.66 | 2900 | 1.6372          |
| 1.2712        | 23.44 | 3000 | 1.6345          |
| 1.2712        | 24.22 | 3100 | 1.6442          |
| 1.2712        | 25.0  | 3200 | 1.6864          |
| 1.2712        | 25.78 | 3300 | 1.6139          |
| 1.2712        | 26.56 | 3400 | 1.6161          |
| 1.215         | 27.34 | 3500 | 1.6491          |
| 1.215         | 28.12 | 3600 | 1.6442          |
| 1.215         | 28.91 | 3700 | 1.6409          |
| 1.215         | 29.69 | 3800 | 1.6539          |
| 1.215         | 30.47 | 3900 | 1.6052          |
| 1.1652        | 31.25 | 4000 | 1.6459          |
| 1.1652        | 32.03 | 4100 | 1.6362          |
| 1.1652        | 32.81 | 4200 | 1.6413          |
| 1.1652        | 33.59 | 4300 | 1.6377          |
| 1.1652        | 34.38 | 4400 | 1.6344          |
| 1.1213        | 35.16 | 4500 | 1.6406          |
| 1.1213        | 35.94 | 4600 | 1.6113          |
| 1.1213        | 36.72 | 4700 | 1.6410          |
| 1.1213        | 37.5  | 4800 | 1.6378          |
| 1.1213        | 38.28 | 4900 | 1.6341          |
| 1.0939        | 39.06 | 5000 | 1.6020          |


### Framework versions

- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
