README.md

metadata

base_model: monologg/biobert_v1.1_pubmed
tags:
  - generated_from_trainer
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: NLP-HIBA_DisTEMIST_fine_tuned_biobert
    results: []

NLP-HIBA_DisTEMIST_fine_tuned_biobert

This model is a fine-tuned version of monologg/biobert_v1.1_pubmed on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.2325
• Precision: 0.5394
• Recall: 0.5010
• F1: 0.5195
• Accuracy: 0.9434

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: 8
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	1.0	71	0.2200	0.2689	0.2813	0.2750	0.9147
No log	2.0	142	0.1895	0.4126	0.3834	0.3975	0.9308
No log	3.0	213	0.1813	0.5319	0.3667	0.4341	0.9389
No log	4.0	284	0.1746	0.5085	0.4395	0.4715	0.9410
No log	5.0	355	0.1942	0.4868	0.4697	0.4781	0.9382
No log	6.0	426	0.1975	0.5246	0.4860	0.5046	0.9413
No log	7.0	497	0.2066	0.5207	0.4897	0.5047	0.9415
0.122	8.0	568	0.2201	0.5232	0.4956	0.5090	0.9420
0.122	9.0	639	0.2358	0.5178	0.5048	0.5112	0.9418
0.122	10.0	710	0.2325	0.5394	0.5010	0.5195	0.9434

Framework versions

• Transformers 4.35.0
• Pytorch 2.1.0+cu118
• Datasets 2.14.6
• Tokenizers 0.14.1