|
--- |
|
tags: |
|
- generated_from_trainer |
|
datasets: |
|
- ncbi_disease |
|
metrics: |
|
- precision |
|
- recall |
|
- f1 |
|
- accuracy |
|
model-index: |
|
- name: BioBERT-mnli-snli-scinli-scitail-mednli-stsb-ncbi |
|
results: |
|
- task: |
|
name: Token Classification |
|
type: token-classification |
|
dataset: |
|
name: ncbi_disease |
|
type: ncbi_disease |
|
config: ncbi_disease |
|
split: test |
|
args: ncbi_disease |
|
metrics: |
|
- name: Precision |
|
type: precision |
|
value: 0.8604187437686939 |
|
- name: Recall |
|
type: recall |
|
value: 0.8989583333333333 |
|
- name: F1 |
|
type: f1 |
|
value: 0.879266428935303 |
|
- name: Accuracy |
|
type: accuracy |
|
value: 0.9870188186308527 |
|
--- |
|
|
|
<!-- 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-mnli-snli-scinli-scitail-mednli-stsb-ncbi |
|
|
|
This model is a fine-tuned version of [pritamdeka/BioBERT-mnli-snli-scinli-scitail-mednli-stsb](https://huggingface.co/pritamdeka/BioBERT-mnli-snli-scinli-scitail-mednli-stsb) on the ncbi_disease dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.0814 |
|
- Precision: 0.8604 |
|
- Recall: 0.8990 |
|
- F1: 0.8793 |
|
- Accuracy: 0.9870 |
|
|
|
## 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: 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: 10 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
|
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
|
| No log | 1.0 | 340 | 0.0481 | 0.8308 | 0.8438 | 0.8372 | 0.9840 | |
|
| 0.0715 | 2.0 | 680 | 0.0497 | 0.8337 | 0.8771 | 0.8548 | 0.9857 | |
|
| 0.0152 | 3.0 | 1020 | 0.0588 | 0.8596 | 0.8802 | 0.8698 | 0.9858 | |
|
| 0.0152 | 4.0 | 1360 | 0.0589 | 0.8589 | 0.8875 | 0.8730 | 0.9873 | |
|
| 0.0059 | 5.0 | 1700 | 0.0693 | 0.8412 | 0.8938 | 0.8667 | 0.9852 | |
|
| 0.003 | 6.0 | 2040 | 0.0770 | 0.8701 | 0.9 | 0.8848 | 0.9863 | |
|
| 0.003 | 7.0 | 2380 | 0.0787 | 0.861 | 0.8969 | 0.8786 | 0.9863 | |
|
| 0.0014 | 8.0 | 2720 | 0.0760 | 0.8655 | 0.8979 | 0.8814 | 0.9872 | |
|
| 0.0007 | 9.0 | 3060 | 0.0817 | 0.8589 | 0.8938 | 0.8760 | 0.9865 | |
|
| 0.0007 | 10.0 | 3400 | 0.0814 | 0.8604 | 0.8990 | 0.8793 | 0.9870 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.29.1 |
|
- Pytorch 2.0.1+cpu |
|
- Datasets 2.12.0 |
|
- Tokenizers 0.13.3 |
|
|