README.md

metadata

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: []

NHS-BiomedNLP-BiomedBERT-hypop-512

This model is a fine-tuned version of 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