transformers pytorch tensorboard bert token-classification generated_from_trainer dataset:ncbi_disease model-index autotrain_compatible endpoints_compatible region:us
---
datasets:
- ncbi_disease
metrics:
- precision
- recall
- f1
- accuracy
tags:
- generated_from_trainer
model-index:
- name: BioBERT-mnli-snli-scinli-scitail-mednli-stsb-ncbi
  results:
  - task:
      type: token-classification
      name: Token Classification
    dataset:
      name: ncbi_disease
      type: ncbi_disease
      config: ncbi_disease
      split: test
      args: ncbi_disease
    metrics:
    - type: precision
      value: 0.8604187437686939
      name: Precision
    - type: recall
      value: 0.8989583333333333
      name: Recall
    - type: f1
      value: 0.879266428935303
      name: F1
    - type: accuracy
      value: 0.9870188186308527
      name: Accuracy
---

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# BioBERT-mnli-snli-scinli-scitail-mednli-stsb-ncbi

This model is a fine-tuned version of [pritamdeka/BioBERT-mnli-snli-scinli-scitail-mednli-stsb](https://huggingface.co/pritamdeka/BioBERT-mnli-snli-scinli-scitail-mednli-stsb) on the ncbi_disease dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0814
- Precision: 0.8604
- Recall: 0.8990
- F1: 0.8793
- Accuracy: 0.9870

## 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: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 340  | 0.0481          | 0.8308    | 0.8438 | 0.8372 | 0.9840   |
| 0.0715        | 2.0   | 680  | 0.0497          | 0.8337    | 0.8771 | 0.8548 | 0.9857   |
| 0.0152        | 3.0   | 1020 | 0.0588          | 0.8596    | 0.8802 | 0.8698 | 0.9858   |
| 0.0152        | 4.0   | 1360 | 0.0589          | 0.8589    | 0.8875 | 0.8730 | 0.9873   |
| 0.0059        | 5.0   | 1700 | 0.0693          | 0.8412    | 0.8938 | 0.8667 | 0.9852   |
| 0.003         | 6.0   | 2040 | 0.0770          | 0.8701    | 0.9    | 0.8848 | 0.9863   |
| 0.003         | 7.0   | 2380 | 0.0787          | 0.861     | 0.8969 | 0.8786 | 0.9863   |
| 0.0014        | 8.0   | 2720 | 0.0760          | 0.8655    | 0.8979 | 0.8814 | 0.9872   |
| 0.0007        | 9.0   | 3060 | 0.0817          | 0.8589    | 0.8938 | 0.8760 | 0.9865   |
| 0.0007        | 10.0  | 3400 | 0.0814          | 0.8604    | 0.8990 | 0.8793 | 0.9870   |


### Framework versions

- Transformers 4.29.1
- Pytorch 2.0.1+cpu
- Datasets 2.12.0
- Tokenizers 0.13.3
