---
license: mit
base_model: microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: NHS-BiomedNLP-BiomedBERT-hypop-512
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# NHS-BiomedNLP-BiomedBERT-hypop-512

This model is a fine-tuned version of [microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract](https://huggingface.co/microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3839
- Accuracy: 0.8269
- Precision: 0.8228
- Recall: 0.8237
- F1: 0.8232

## 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: 3e-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: 6

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.124         | 1.0   | 397  | 0.4029          | 0.8177   | 0.8146    | 0.8129 | 0.8137 |
| 0.0594        | 2.0   | 794  | 0.4561          | 0.8246   | 0.8245    | 0.8161 | 0.8192 |
| 0.1105        | 3.0   | 1191 | 0.5390          | 0.8120   | 0.8119    | 0.8028 | 0.8059 |


### Framework versions

- Transformers 4.38.2
- Pytorch 2.2.2+cpu
- Datasets 2.18.0
- Tokenizers 0.15.2