|
--- |
|
tags: |
|
- generated_from_trainer |
|
datasets: |
|
- jnlpba |
|
metrics: |
|
- precision |
|
- recall |
|
- f1 |
|
- accuracy |
|
model-index: |
|
- name: scibert-finetuned-ner |
|
results: |
|
- task: |
|
name: Token Classification |
|
type: token-classification |
|
dataset: |
|
name: jnlpba |
|
type: jnlpba |
|
config: jnlpba |
|
split: train |
|
args: jnlpba |
|
metrics: |
|
- name: Precision |
|
type: precision |
|
value: 0.6737190414118119 |
|
- name: Recall |
|
type: recall |
|
value: 0.7756869083352574 |
|
- name: F1 |
|
type: f1 |
|
value: 0.7211161792326267 |
|
- name: Accuracy |
|
type: accuracy |
|
value: 0.9226268866380928 |
|
--- |
|
|
|
<!-- 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-finetuned-ner |
|
|
|
This model is a fine-tuned version of [allenai/scibert_scivocab_uncased](https://huggingface.co/allenai/scibert_scivocab_uncased) on the jnlpba dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.4717 |
|
- Precision: 0.6737 |
|
- Recall: 0.7757 |
|
- F1: 0.7211 |
|
- Accuracy: 0.9226 |
|
|
|
## 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: 5 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
|
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:| |
|
| 0.1608 | 1.0 | 2319 | 0.2431 | 0.6641 | 0.7581 | 0.7080 | 0.9250 | |
|
| 0.103 | 2.0 | 4638 | 0.2916 | 0.6739 | 0.7803 | 0.7232 | 0.9228 | |
|
| 0.0659 | 3.0 | 6957 | 0.3662 | 0.6796 | 0.7624 | 0.7186 | 0.9233 | |
|
| 0.0393 | 4.0 | 9276 | 0.4222 | 0.6737 | 0.7771 | 0.7217 | 0.9225 | |
|
| 0.025 | 5.0 | 11595 | 0.4717 | 0.6737 | 0.7757 | 0.7211 | 0.9226 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.21.1 |
|
- Pytorch 1.12.1+cu113 |
|
- Datasets 2.4.0 |
|
- Tokenizers 0.12.1 |
|
|
