metadata
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: im-bin-tf-abstr
results: []
im-bin-tf-abstr
This model is a fine-tuned version of microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.1823
- Accuracy: 0.9261
- F1: 0.9261
- Precision: 0.9285
- Recall: 0.9237
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: 1e-05
- train_batch_size: 512
- eval_batch_size: 1024
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 4
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 469 | 0.1895 | 0.9226 | 0.9222 | 0.9301 | 0.9145 |
| 0.1886 | 2.0 | 938 | 0.1844 | 0.9251 | 0.9253 | 0.9259 | 0.9247 |
| 0.1688 | 3.0 | 1407 | 0.1823 | 0.9261 | 0.9261 | 0.9285 | 0.9237 |
| 0.1558 | 4.0 | 1876 | 0.1838 | 0.9259 | 0.9261 | 0.9262 | 0.9260 |
Framework versions
- Transformers 4.31.0.dev0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3