metadata
license: apache-2.0
base_model: google-bert/bert-base-uncased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: BERT_BIOMAT_NER__ST_1000_DA
results: []
BERT_BIOMAT_NER__ST_1000_DA
This model is a fine-tuned version of google-bert/bert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.4791
- Precision: 0.4732
- Recall: 0.6716
- F1: 0.5552
- Accuracy: 0.9364
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: 32
- eval_batch_size: 32
- 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 | 397 | 0.2764 | 0.4422 | 0.5997 | 0.5090 | 0.9323 |
| 0.2077 | 2.0 | 794 | 0.3185 | 0.4682 | 0.6485 | 0.5438 | 0.9356 |
| 0.0466 | 3.0 | 1191 | 0.3461 | 0.4699 | 0.6592 | 0.5487 | 0.9362 |
| 0.0179 | 4.0 | 1588 | 0.3994 | 0.4567 | 0.6595 | 0.5397 | 0.9342 |
| 0.0179 | 5.0 | 1985 | 0.4091 | 0.4735 | 0.6733 | 0.5560 | 0.9369 |
| 0.0088 | 6.0 | 2382 | 0.4392 | 0.4701 | 0.6630 | 0.5501 | 0.9366 |
| 0.0048 | 7.0 | 2779 | 0.4594 | 0.4654 | 0.6644 | 0.5473 | 0.9356 |
| 0.0032 | 8.0 | 3176 | 0.4684 | 0.4740 | 0.6775 | 0.5578 | 0.9369 |
| 0.0024 | 9.0 | 3573 | 0.4763 | 0.4703 | 0.6623 | 0.5500 | 0.9359 |
| 0.0024 | 10.0 | 3970 | 0.4791 | 0.4732 | 0.6716 | 0.5552 | 0.9364 |
Framework versions
- Transformers 4.42.4
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1