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---
base_model: monologg/biobert_v1.1_pubmed
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: NLP-HIBA_DisTEMIST_fine_tuned_biobert
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_DisTEMIST_fine_tuned_biobert
This model is a fine-tuned version of [monologg/biobert_v1.1_pubmed](https://huggingface.co/monologg/biobert_v1.1_pubmed) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2325
- Precision: 0.5394
- Recall: 0.5010
- F1: 0.5195
- Accuracy: 0.9434
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 71 | 0.2200 | 0.2689 | 0.2813 | 0.2750 | 0.9147 |
| No log | 2.0 | 142 | 0.1895 | 0.4126 | 0.3834 | 0.3975 | 0.9308 |
| No log | 3.0 | 213 | 0.1813 | 0.5319 | 0.3667 | 0.4341 | 0.9389 |
| No log | 4.0 | 284 | 0.1746 | 0.5085 | 0.4395 | 0.4715 | 0.9410 |
| No log | 5.0 | 355 | 0.1942 | 0.4868 | 0.4697 | 0.4781 | 0.9382 |
| No log | 6.0 | 426 | 0.1975 | 0.5246 | 0.4860 | 0.5046 | 0.9413 |
| No log | 7.0 | 497 | 0.2066 | 0.5207 | 0.4897 | 0.5047 | 0.9415 |
| 0.122 | 8.0 | 568 | 0.2201 | 0.5232 | 0.4956 | 0.5090 | 0.9420 |
| 0.122 | 9.0 | 639 | 0.2358 | 0.5178 | 0.5048 | 0.5112 | 0.9418 |
| 0.122 | 10.0 | 710 | 0.2325 | 0.5394 | 0.5010 | 0.5195 | 0.9434 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
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