transformers tensorboard safetensors distilbert token-classification generated_from_trainer base_model:medicalai/ClinicalBERT base_model:finetune:medicalai/ClinicalBERT autotrain_compatible endpoints_compatible region:us
---
base_model: medicalai/ClinicalBERT
metrics:
- precision
- recall
- f1
- accuracy
tags:
- generated_from_trainer
model-index:
- name: ClinicalBERT_BC5CDR_NER_new
  results: []
---

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# ClinicalBERT_BC5CDR_NER_new

This model is a fine-tuned version of [medicalai/ClinicalBERT](https://huggingface.co/medicalai/ClinicalBERT) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1097
- Precision: 0.7957
- Recall: 0.8166
- F1: 0.8060
- Accuracy: 0.9658

## 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: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 286  | 0.1154          | 0.7710    | 0.7821 | 0.7765 | 0.9611   |
| 0.145         | 2.0   | 572  | 0.1097          | 0.7756    | 0.8176 | 0.7961 | 0.9645   |
| 0.145         | 3.0   | 858  | 0.1097          | 0.7957    | 0.8166 | 0.8060 | 0.9658   |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
