transformers pytorch tensorboard gpt2 token-classification generated_from_trainer dataset:ncbi_disease model-index autotrain_compatible text-generation-inference endpoints_compatible region:us
---
datasets:
- ncbi_disease
metrics:
- precision
- recall
- f1
- accuracy
tags:
- generated_from_trainer
model-index:
- name: biogpt
  results:
  - task:
      type: token-classification
      name: Token Classification
    dataset:
      name: ncbi_disease
      type: ncbi_disease
      config: ncbi_disease
      split: validation[:-1]
      args: ncbi_disease
    metrics:
    - type: precision
      value: 0.5170124481327801
      name: Precision
    - type: recall
      value: 0.6013513513513513
      name: Recall
    - type: f1
      value: 0.5560017849174477
      name: F1
    - type: accuracy
      value: 0.9555546552143263
      name: Accuracy
---

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# biogpt

This model was trained from scratch on the ncbi_disease dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1599
- Precision: 0.5170
- Recall: 0.6014
- F1: 0.5560
- Accuracy: 0.9556

## 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: 0.0001
- 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   | 340  | 0.1765          | 0.3914    | 0.5946 | 0.4720 | 0.9425   |
| 0.2426        | 2.0   | 680  | 0.1538          | 0.4769    | 0.6091 | 0.5350 | 0.9514   |
| 0.0881        | 3.0   | 1020 | 0.1599          | 0.5170    | 0.6014 | 0.5560 | 0.9556   |


### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
