metadata
license: mit
base_model: microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: PubMedBERT_BioNLP13CG_NER_new
results: []
PubMedBERT_BioNLP13CG_NER_new
This model is a fine-tuned version of microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.1724
- Precision: 0.8806
- Recall: 0.8773
- F1: 0.8789
- Accuracy: 0.9595
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: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 191 | 0.2269 | 0.8404 | 0.8521 | 0.8462 | 0.9468 |
| No log | 2.0 | 382 | 0.1772 | 0.8728 | 0.8710 | 0.8719 | 0.9574 |
| 0.362 | 3.0 | 573 | 0.1724 | 0.8806 | 0.8773 | 0.8789 | 0.9595 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0