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---
base_model: dmis-lab/biobert-base-cased-v1.2
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: relation-biobert-biocause
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# relation-biobert-biocause
This model is a fine-tuned version of [dmis-lab/biobert-base-cased-v1.2](https://huggingface.co/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
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