|
--- |
|
tags: |
|
- generated_from_trainer |
|
metrics: |
|
- accuracy |
|
model-index: |
|
- name: pritamdeka/PubMedBERT-MNLI-MEDNLI |
|
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. --> |
|
|
|
# pritamdeka/PubMedBERT-MNLI-MEDNLI |
|
|
|
This model is a fine-tuned version of [PubMedBERT](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext) on the [MNLI](https://huggingface.co/datasets/multi_nli) dataset first and then on the [MedNLI](https://physionet.org/content/mednli/1.0.0/) dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.9501 |
|
- Accuracy: 0.8667 |
|
|
|
## 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: 32 |
|
- eval_batch_size: 32 |
|
- seed: 42 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: linear |
|
- num_epochs: 20.0 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
|
|:-------------:|:-----:|:----:|:---------------:|:--------:| |
|
| 0.5673 | 1.42 | 500 | 0.4358 | 0.8437 | |
|
| 0.2898 | 2.85 | 1000 | 0.4845 | 0.8523 | |
|
| 0.1669 | 4.27 | 1500 | 0.6233 | 0.8573 | |
|
| 0.1087 | 5.7 | 2000 | 0.7263 | 0.8573 | |
|
| 0.0728 | 7.12 | 2500 | 0.8841 | 0.8638 | |
|
| 0.0512 | 8.55 | 3000 | 0.9501 | 0.8667 | |
|
| 0.0372 | 9.97 | 3500 | 1.0440 | 0.8566 | |
|
| 0.0262 | 11.4 | 4000 | 1.0770 | 0.8609 | |
|
| 0.0243 | 12.82 | 4500 | 1.0931 | 0.8616 | |
|
| 0.023 | 14.25 | 5000 | 1.1088 | 0.8631 | |
|
| 0.0163 | 15.67 | 5500 | 1.1264 | 0.8581 | |
|
| 0.0111 | 17.09 | 6000 | 1.1541 | 0.8616 | |
|
| 0.0098 | 18.52 | 6500 | 1.1542 | 0.8631 | |
|
| 0.0074 | 19.94 | 7000 | 1.1653 | 0.8638 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.22.0.dev0 |
|
- Pytorch 1.12.1+cu113 |
|
- Datasets 2.4.0 |
|
- Tokenizers 0.12.1 |
|
|
