File size: 2,726 Bytes
705c48d 897ed4d 705c48d 897ed4d 705c48d 897ed4d 705c48d |
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 76 77 78 |
---
license: mit
base_model: microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: NLP-HIBA_BiomedNLP-BiomedBERT-base-pretrained-model
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. -->
# NLP-HIBA_BiomedNLP-BiomedBERT-base-pretrained-model
This model is a fine-tuned version of [microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext](https://huggingface.co/microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2050
- Precision: 0.6079
- Recall: 0.5407
- F1: 0.5723
- Accuracy: 0.9528
## 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: 12
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 71 | 0.2223 | 0.3125 | 0.1619 | 0.2133 | 0.9212 |
| No log | 2.0 | 142 | 0.1599 | 0.5228 | 0.3539 | 0.4221 | 0.9446 |
| No log | 3.0 | 213 | 0.1472 | 0.5298 | 0.4385 | 0.4798 | 0.9470 |
| No log | 4.0 | 284 | 0.1441 | 0.5885 | 0.4729 | 0.5244 | 0.9514 |
| No log | 5.0 | 355 | 0.1675 | 0.5654 | 0.5146 | 0.5388 | 0.9491 |
| No log | 6.0 | 426 | 0.1592 | 0.5860 | 0.5082 | 0.5443 | 0.9521 |
| No log | 7.0 | 497 | 0.1634 | 0.5621 | 0.5587 | 0.5604 | 0.9509 |
| 0.1349 | 8.0 | 568 | 0.1897 | 0.5803 | 0.5182 | 0.5475 | 0.9515 |
| 0.1349 | 9.0 | 639 | 0.1880 | 0.5699 | 0.5539 | 0.5618 | 0.9506 |
| 0.1349 | 10.0 | 710 | 0.1939 | 0.5923 | 0.5415 | 0.5657 | 0.9525 |
| 0.1349 | 11.0 | 781 | 0.1988 | 0.5863 | 0.5475 | 0.5662 | 0.9518 |
| 0.1349 | 12.0 | 852 | 0.2050 | 0.6079 | 0.5407 | 0.5723 | 0.9528 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|