|
--- |
|
license: apache-2.0 |
|
tags: |
|
- generated_from_trainer |
|
datasets: |
|
- conll2003 |
|
metrics: |
|
- precision |
|
- recall |
|
- f1 |
|
- accuracy |
|
model-index: |
|
- name: bert-finetuned-ner |
|
results: |
|
- task: |
|
name: Token Classification |
|
type: token-classification |
|
dataset: |
|
name: conll2003 |
|
type: conll2003 |
|
config: conll2003 |
|
split: validation |
|
args: conll2003 |
|
metrics: |
|
- name: Precision |
|
type: precision |
|
value: 0.9415670650730412 |
|
- name: Recall |
|
type: recall |
|
value: 0.9545607539548974 |
|
- name: F1 |
|
type: f1 |
|
value: 0.9480193882667558 |
|
- name: Accuracy |
|
type: accuracy |
|
value: 0.9869311826690998 |
|
--- |
|
|
|
<!-- 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. --> |
|
|
|
# bert-finetuned-ner |
|
|
|
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.0822 |
|
- Precision: 0.9416 |
|
- Recall: 0.9546 |
|
- F1: 0.9480 |
|
- Accuracy: 0.9869 |
|
|
|
## 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: 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: 6 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
|
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:| |
|
| 0.0911 | 1.0 | 1756 | 0.0656 | 0.9223 | 0.9372 | 0.9297 | 0.9827 | |
|
| 0.0342 | 2.0 | 3512 | 0.0667 | 0.9259 | 0.9456 | 0.9356 | 0.9851 | |
|
| 0.0203 | 3.0 | 5268 | 0.0705 | 0.9195 | 0.9419 | 0.9306 | 0.9837 | |
|
| 0.0143 | 4.0 | 7024 | 0.0685 | 0.9340 | 0.9500 | 0.9419 | 0.9858 | |
|
| 0.0083 | 5.0 | 8780 | 0.0775 | 0.9362 | 0.9515 | 0.9438 | 0.9864 | |
|
| 0.0027 | 6.0 | 10536 | 0.0822 | 0.9416 | 0.9546 | 0.9480 | 0.9869 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.29.2 |
|
- Pytorch 2.0.1+cu118 |
|
- Datasets 2.12.0 |
|
- Tokenizers 0.13.3 |
|
|
