|
--- |
|
license: mit |
|
base_model: xlm-roberta-large |
|
tags: |
|
- generated_from_trainer |
|
datasets: |
|
- conll2003job |
|
metrics: |
|
- precision |
|
- recall |
|
- f1 |
|
- accuracy |
|
model-index: |
|
- name: my_xlm-roberta-large-finetuned-conlljob04 |
|
results: |
|
- task: |
|
name: Token Classification |
|
type: token-classification |
|
dataset: |
|
name: conll2003job |
|
type: conll2003job |
|
config: conll2003job |
|
split: validation |
|
args: conll2003job |
|
metrics: |
|
- name: Precision |
|
type: precision |
|
value: 0.961673640167364 |
|
- name: Recall |
|
type: recall |
|
value: 0.9670144732413329 |
|
- name: F1 |
|
type: f1 |
|
value: 0.964336661911555 |
|
- name: Accuracy |
|
type: accuracy |
|
value: 0.9935750165491998 |
|
--- |
|
|
|
<!-- 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. --> |
|
|
|
# my_xlm-roberta-large-finetuned-conlljob04 |
|
|
|
This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on the conll2003job dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.0420 |
|
- Precision: 0.9617 |
|
- Recall: 0.9670 |
|
- F1: 0.9643 |
|
- Accuracy: 0.9936 |
|
|
|
## 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: 6 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
|
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
|
| 0.1566 | 1.0 | 896 | 0.0403 | 0.9425 | 0.9542 | 0.9483 | 0.9911 | |
|
| 0.0319 | 2.0 | 1792 | 0.0359 | 0.9523 | 0.9571 | 0.9547 | 0.9922 | |
|
| 0.0156 | 3.0 | 2688 | 0.0356 | 0.9594 | 0.9625 | 0.9609 | 0.9929 | |
|
| 0.01 | 4.0 | 3584 | 0.0377 | 0.9604 | 0.9672 | 0.9638 | 0.9934 | |
|
| 0.0058 | 5.0 | 4480 | 0.0398 | 0.9618 | 0.9662 | 0.9640 | 0.9934 | |
|
| 0.0034 | 6.0 | 5376 | 0.0420 | 0.9617 | 0.9670 | 0.9643 | 0.9936 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.34.1 |
|
- Pytorch 2.1.0+cu118 |
|
- Datasets 2.14.5 |
|
- Tokenizers 0.14.1 |
|
|
