relation-biobert-biocause
This model is a fine-tuned version of dmis-lab/biobert-base-cased-v1.2 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.2103
- Precision: 0.1164
- Recall: 0.625
- F1: 0.1963
- Accuracy: 0.9448
- Relation P: 0.1164
- Relation R: 0.625
- Relation F1: 0.1963
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: 4e-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: 1
Training results
Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | Relation P | Relation R | Relation F1 |
---|---|---|---|---|---|---|---|---|---|---|
0.6563 | 0.1282 | 20 | 0.2984 | 0.0211 | 0.2105 | 0.0384 | 0.8265 | 0.0211 | 0.2105 | 0.0384 |
0.6563 | 0.2564 | 40 | 0.2302 | 0.0763 | 0.4605 | 0.1308 | 0.9266 | 0.0763 | 0.4605 | 0.1308 |
0.6563 | 0.3846 | 60 | 0.4003 | 0.1406 | 0.5921 | 0.2273 | 0.9617 | 0.1406 | 0.5921 | 0.2273 |
0.6563 | 0.5128 | 80 | 0.2185 | 0.0554 | 0.4671 | 0.0990 | 0.8933 | 0.0554 | 0.4671 | 0.0990 |
0.6563 | 0.6410 | 100 | 0.2261 | 0.1345 | 0.7105 | 0.2262 | 0.9510 | 0.1345 | 0.7105 | 0.2262 |
0.6563 | 0.7692 | 120 | 0.2315 | 0.1259 | 0.6579 | 0.2114 | 0.9502 | 0.1259 | 0.6579 | 0.2114 |
0.6563 | 0.8974 | 140 | 0.2324 | 0.1417 | 0.6711 | 0.2339 | 0.9547 | 0.1417 | 0.6711 | 0.2339 |
Framework versions
- Transformers 4.42.4
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
- Downloads last month
- 3
Model tree for alenatz/relation-biobert-biocause
Base model
dmis-lab/biobert-base-cased-v1.2