File size: 2,331 Bytes
6a5cd5c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 |
---
base_model: dmis-lab/biobert-v1.1
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: biobert-all
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. -->
# biobert-all
This model is a fine-tuned version of [dmis-lab/biobert-v1.1](https://huggingface.co/dmis-lab/biobert-v1.1) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7750
- Precision: 0.5990
- Recall: 0.6572
- F1: 0.6268
- Accuracy: 0.8385
## 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: 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 | 363 | 0.4337 | 0.5819 | 0.6535 | 0.6156 | 0.8427 |
| 0.4325 | 2.0 | 726 | 0.4422 | 0.5912 | 0.6675 | 0.6270 | 0.8438 |
| 0.2832 | 3.0 | 1089 | 0.4720 | 0.6010 | 0.6422 | 0.6209 | 0.8443 |
| 0.2832 | 4.0 | 1452 | 0.5342 | 0.6076 | 0.6522 | 0.6291 | 0.8454 |
| 0.1948 | 5.0 | 1815 | 0.5969 | 0.6059 | 0.6594 | 0.6315 | 0.8415 |
| 0.1315 | 6.0 | 2178 | 0.6428 | 0.6051 | 0.6551 | 0.6291 | 0.8408 |
| 0.0987 | 7.0 | 2541 | 0.6933 | 0.5933 | 0.6649 | 0.6270 | 0.8384 |
| 0.0987 | 8.0 | 2904 | 0.7353 | 0.5949 | 0.6633 | 0.6273 | 0.8390 |
| 0.0762 | 9.0 | 3267 | 0.7640 | 0.5920 | 0.6623 | 0.6252 | 0.8389 |
| 0.0628 | 10.0 | 3630 | 0.7750 | 0.5990 | 0.6572 | 0.6268 | 0.8385 |
### Framework versions
- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
|