---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: best_model
  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. -->

# best_model

This model is a fine-tuned version of [microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2833
- Accuracy: 0.8942

## 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: 5e-05
- 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
- num_epochs: 2.0

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.3604        | 0.14  | 5000  | 0.3162          | 0.8821   |
| 0.3326        | 0.29  | 10000 | 0.3112          | 0.8843   |
| 0.3293        | 0.43  | 15000 | 0.3044          | 0.8870   |
| 0.3246        | 0.58  | 20000 | 0.3040          | 0.8871   |
| 0.32          | 0.72  | 25000 | 0.2969          | 0.8888   |
| 0.3143        | 0.87  | 30000 | 0.2929          | 0.8903   |
| 0.3095        | 1.01  | 35000 | 0.2917          | 0.8899   |
| 0.2844        | 1.16  | 40000 | 0.2957          | 0.8886   |
| 0.2778        | 1.3   | 45000 | 0.2943          | 0.8906   |
| 0.2779        | 1.45  | 50000 | 0.2890          | 0.8935   |
| 0.2752        | 1.59  | 55000 | 0.2881          | 0.8919   |
| 0.2736        | 1.74  | 60000 | 0.2835          | 0.8944   |
| 0.2725        | 1.88  | 65000 | 0.2833          | 0.8942   |


### Framework versions

- Transformers 4.18.0.dev0
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.6