|
--- |
|
tags: |
|
- generated_from_trainer |
|
datasets: |
|
- wnut_17 |
|
metrics: |
|
- precision |
|
- recall |
|
- f1 |
|
- accuracy |
|
model-index: |
|
- name: twitter-roberta-base-WNUT |
|
results: |
|
- task: |
|
name: Token Classification |
|
type: token-classification |
|
dataset: |
|
name: wnut_17 |
|
type: wnut_17 |
|
args: wnut_17 |
|
metrics: |
|
- name: Precision |
|
type: precision |
|
value: 0.7045454545454546 |
|
- name: Recall |
|
type: recall |
|
value: 0.6303827751196173 |
|
- name: F1 |
|
type: f1 |
|
value: 0.6654040404040403 |
|
- name: Accuracy |
|
type: accuracy |
|
value: 0.9639611008707811 |
|
--- |
|
|
|
<!-- 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. --> |
|
|
|
# twitter-roberta-base-WNUT |
|
|
|
This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base](https://huggingface.co/cardiffnlp/twitter-roberta-base) on the wnut_17 dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.1938 |
|
- Precision: 0.7045 |
|
- Recall: 0.6304 |
|
- F1: 0.6654 |
|
- Accuracy: 0.9640 |
|
|
|
## 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: 64 |
|
- eval_batch_size: 1024 |
|
- 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 | 0.46 | 25 | 0.3912 | 0.0 | 0.0 | 0.0 | 0.9205 | |
|
| No log | 0.93 | 50 | 0.2847 | 0.25 | 0.0024 | 0.0047 | 0.9209 | |
|
| No log | 1.39 | 75 | 0.2449 | 0.5451 | 0.3469 | 0.4240 | 0.9426 | |
|
| No log | 1.85 | 100 | 0.1946 | 0.6517 | 0.4856 | 0.5565 | 0.9492 | |
|
| No log | 2.31 | 125 | 0.1851 | 0.6921 | 0.5646 | 0.6219 | 0.9581 | |
|
| No log | 2.78 | 150 | 0.1672 | 0.6867 | 0.5873 | 0.6331 | 0.9594 | |
|
| No log | 3.24 | 175 | 0.1675 | 0.6787 | 0.5837 | 0.6277 | 0.9615 | |
|
| No log | 3.7 | 200 | 0.1644 | 0.6765 | 0.6328 | 0.6539 | 0.9638 | |
|
| No log | 4.17 | 225 | 0.1672 | 0.6997 | 0.6495 | 0.6737 | 0.9640 | |
|
| No log | 4.63 | 250 | 0.1652 | 0.6915 | 0.6435 | 0.6667 | 0.9649 | |
|
| No log | 5.09 | 275 | 0.1882 | 0.7067 | 0.6053 | 0.6521 | 0.9629 | |
|
| No log | 5.56 | 300 | 0.1783 | 0.7128 | 0.6352 | 0.6717 | 0.9645 | |
|
| No log | 6.02 | 325 | 0.1813 | 0.7011 | 0.6172 | 0.6565 | 0.9639 | |
|
| No log | 6.48 | 350 | 0.1804 | 0.7139 | 0.6447 | 0.6776 | 0.9647 | |
|
| No log | 6.94 | 375 | 0.1902 | 0.7218 | 0.6268 | 0.6709 | 0.9641 | |
|
| No log | 7.41 | 400 | 0.1883 | 0.7106 | 0.6316 | 0.6688 | 0.9641 | |
|
| No log | 7.87 | 425 | 0.1862 | 0.7067 | 0.6340 | 0.6683 | 0.9643 | |
|
| No log | 8.33 | 450 | 0.1882 | 0.7053 | 0.6328 | 0.6671 | 0.9639 | |
|
| No log | 8.8 | 475 | 0.1919 | 0.7055 | 0.6304 | 0.6658 | 0.9638 | |
|
| 0.1175 | 9.26 | 500 | 0.1938 | 0.7045 | 0.6304 | 0.6654 | 0.9640 | |
|
| 0.1175 | 9.72 | 525 | 0.1880 | 0.7025 | 0.6411 | 0.6704 | 0.9646 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.20.1 |
|
- Pytorch 1.12.0 |
|
- Datasets 2.3.2 |
|
- Tokenizers 0.12.1 |
|
|
