|
--- |
|
license: mit |
|
base_model: roberta-base |
|
tags: |
|
- generated_from_trainer |
|
metrics: |
|
- accuracy |
|
- precision |
|
- recall |
|
- f1 |
|
model-index: |
|
- name: irony_fr_Canada |
|
results: [] |
|
--- |
|
|
|
<!-- 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. --> |
|
|
|
# irony_fr_Canada |
|
|
|
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.0034 |
|
- Accuracy: 0.6647 |
|
- Precision: 0.4748 |
|
- Recall: 0.6111 |
|
- F1: 0.5344 |
|
|
|
## 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: 5e-06 |
|
- train_batch_size: 16 |
|
- eval_batch_size: 64 |
|
- seed: 42 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: linear |
|
- num_epochs: 20 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |
|
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| |
|
| 0.0044 | 1.0 | 65 | 0.0043 | 0.6764 | 0.4 | 0.0556 | 0.0976 | |
|
| 0.0042 | 2.0 | 130 | 0.0042 | 0.5773 | 0.4 | 0.6852 | 0.5051 | |
|
| 0.0039 | 3.0 | 195 | 0.0040 | 0.6618 | 0.4574 | 0.3981 | 0.4257 | |
|
| 0.0037 | 4.0 | 260 | 0.0036 | 0.6356 | 0.4422 | 0.6019 | 0.5098 | |
|
| 0.003 | 5.0 | 325 | 0.0033 | 0.5889 | 0.4108 | 0.7037 | 0.5188 | |
|
| 0.0026 | 6.0 | 390 | 0.0033 | 0.6647 | 0.4706 | 0.5185 | 0.4934 | |
|
| 0.0022 | 7.0 | 455 | 0.0034 | 0.6647 | 0.4748 | 0.6111 | 0.5344 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.34.1 |
|
- Pytorch 2.0.1+cu117 |
|
- Datasets 2.14.5 |
|
- Tokenizers 0.14.1 |
|
|
