|
--- |
|
license: apache-2.0 |
|
tags: |
|
- generated_from_trainer |
|
datasets: |
|
- wnut_17 |
|
metrics: |
|
- precision |
|
- recall |
|
- f1 |
|
- accuracy |
|
model-index: |
|
- name: bert-base-multilingual-cased-WNUT-ner |
|
results: |
|
- task: |
|
name: Token Classification |
|
type: token-classification |
|
dataset: |
|
name: wnut_17 |
|
type: wnut_17 |
|
config: wnut_17 |
|
split: test |
|
args: wnut_17 |
|
metrics: |
|
- name: Precision |
|
type: precision |
|
value: 0.5913669064748202 |
|
- name: Recall |
|
type: recall |
|
value: 0.3809082483781279 |
|
- name: F1 |
|
type: f1 |
|
value: 0.463359639233371 |
|
- name: Accuracy |
|
type: accuracy |
|
value: 0.9500726682055228 |
|
--- |
|
|
|
<!-- 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-base-multilingual-cased-WNUT-ner |
|
|
|
This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the wnut_17 dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.3832 |
|
- Precision: 0.5914 |
|
- Recall: 0.3809 |
|
- F1: 0.4634 |
|
- Accuracy: 0.9501 |
|
|
|
## 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 | 213 | 0.2791 | 0.6008 | 0.2817 | 0.3836 | 0.9427 | |
|
| No log | 2.0 | 426 | 0.2697 | 0.6520 | 0.3299 | 0.4382 | 0.9479 | |
|
| 0.148 | 3.0 | 639 | 0.2846 | 0.5783 | 0.3661 | 0.4484 | 0.9492 | |
|
| 0.148 | 4.0 | 852 | 0.3032 | 0.6248 | 0.3642 | 0.4602 | 0.9500 | |
|
| 0.0413 | 5.0 | 1065 | 0.3355 | 0.5729 | 0.3568 | 0.4397 | 0.9495 | |
|
| 0.0413 | 6.0 | 1278 | 0.3343 | 0.5714 | 0.3892 | 0.4631 | 0.9501 | |
|
| 0.0413 | 7.0 | 1491 | 0.3522 | 0.5877 | 0.3818 | 0.4629 | 0.9500 | |
|
| 0.0182 | 8.0 | 1704 | 0.3844 | 0.6120 | 0.3698 | 0.4610 | 0.9499 | |
|
| 0.0182 | 9.0 | 1917 | 0.3847 | 0.5986 | 0.3828 | 0.4669 | 0.9504 | |
|
| 0.008 | 10.0 | 2130 | 0.3832 | 0.5914 | 0.3809 | 0.4634 | 0.9501 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.26.0 |
|
- Pytorch 1.13.1+cu117 |
|
- Datasets 2.9.0 |
|
- Tokenizers 0.13.2 |
|
|
