---
license: mit
base_model: microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext
tags:
- generated_from_trainer
datasets:
- ncbi_disease
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: checkpoint-1000
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: ncbi_disease
      type: ncbi_disease
      config: ncbi_disease
      split: test
      args: ncbi_disease
    metrics:
    - name: Precision
      type: precision
      value: 0.8456973293768546
    - name: Recall
      type: recall
      value: 0.890625
    - name: F1
      type: f1
      value: 0.8675799086757991
    - name: Accuracy
      type: accuracy
      value: 0.9850593950279626
---

<!-- 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. -->

# checkpoint-1000

This model is a fine-tuned version of [microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext](https://huggingface.co/microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext) on the ncbi_disease dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0543
- Precision: 0.8457
- Recall: 0.8906
- F1: 0.8676
- Accuracy: 0.9851

## 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: 4

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 340  | 0.0596          | 0.7778    | 0.875  | 0.8235 | 0.9795   |
| 0.0787        | 2.0   | 680  | 0.0416          | 0.8246    | 0.8865 | 0.8544 | 0.9851   |
| 0.0202        | 3.0   | 1020 | 0.0494          | 0.8385    | 0.8812 | 0.8593 | 0.9846   |
| 0.0202        | 4.0   | 1360 | 0.0543          | 0.8457    | 0.8906 | 0.8676 | 0.9851   |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0