|
--- |
|
base_model: allenai/scibert_scivocab_cased |
|
tags: |
|
- generated_from_trainer |
|
metrics: |
|
- precision |
|
- recall |
|
- f1 |
|
- accuracy |
|
model-index: |
|
- name: scibert_ner_drugname |
|
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. --> |
|
|
|
# scibert_ner_drugname |
|
|
|
This model is a fine-tuned version of [allenai/scibert_scivocab_cased](https://huggingface.co/allenai/scibert_scivocab_cased) on an unknown dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.1243 |
|
- Precision: 0.7631 |
|
- Recall: 0.8520 |
|
- F1: 0.8051 |
|
- Accuracy: 0.9722 |
|
|
|
## 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: 1e-05 |
|
- train_batch_size: 1 |
|
- eval_batch_size: 1 |
|
- 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 | |
|
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
|
| 0.0733 | 1.0 | 120 | 0.1176 | 0.6466 | 0.7713 | 0.7035 | 0.9583 | |
|
| 0.0069 | 2.0 | 240 | 0.1126 | 0.6757 | 0.7848 | 0.7261 | 0.9654 | |
|
| 0.0521 | 3.0 | 360 | 0.0949 | 0.7461 | 0.8565 | 0.7975 | 0.9707 | |
|
| 0.0217 | 4.0 | 480 | 0.0972 | 0.7171 | 0.8296 | 0.7692 | 0.9718 | |
|
| 0.001 | 5.0 | 600 | 0.1111 | 0.7422 | 0.8520 | 0.7933 | 0.9707 | |
|
| 0.0044 | 6.0 | 720 | 0.1138 | 0.7664 | 0.8386 | 0.8009 | 0.9715 | |
|
| 0.0011 | 7.0 | 840 | 0.1155 | 0.7449 | 0.8251 | 0.7830 | 0.9699 | |
|
| 0.0006 | 8.0 | 960 | 0.1213 | 0.7344 | 0.8430 | 0.7850 | 0.9716 | |
|
| 0.0289 | 9.0 | 1080 | 0.1238 | 0.7661 | 0.8520 | 0.8068 | 0.9718 | |
|
| 0.0096 | 10.0 | 1200 | 0.1243 | 0.7631 | 0.8520 | 0.8051 | 0.9722 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.38.2 |
|
- Pytorch 2.2.1+cu121 |
|
- Datasets 2.18.0 |
|
- Tokenizers 0.15.2 |
|
|
