|
--- |
|
library_name: transformers |
|
license: apache-2.0 |
|
base_model: distilbert-base-uncased |
|
tags: |
|
- generated_from_trainer |
|
datasets: |
|
- hatexplain |
|
metrics: |
|
- accuracy |
|
- precision |
|
- recall |
|
- f1 |
|
model-index: |
|
- name: finetuned-distilbert-hatexplainV2 |
|
results: |
|
- task: |
|
name: Text Classification |
|
type: text-classification |
|
dataset: |
|
name: hatexplain |
|
type: hatexplain |
|
config: plain_text |
|
split: validation |
|
args: plain_text |
|
metrics: |
|
- name: Accuracy |
|
type: accuracy |
|
value: 0.6845114345114345 |
|
- name: Precision |
|
type: precision |
|
value: 0.6875552661807551 |
|
- name: Recall |
|
type: recall |
|
value: 0.6845114345114345 |
|
- name: F1 |
|
type: f1 |
|
value: 0.6848681152421926 |
|
--- |
|
|
|
<!-- 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. --> |
|
|
|
# finetuned-distilbert-hatexplainV2 |
|
|
|
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the hatexplain dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 1.1306 |
|
- Accuracy: 0.6845 |
|
- Precision: 0.6876 |
|
- Recall: 0.6845 |
|
- F1: 0.6849 |
|
|
|
## 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-05 |
|
- train_batch_size: 16 |
|
- eval_batch_size: 16 |
|
- seed: 42 |
|
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
|
- lr_scheduler_type: linear |
|
- num_epochs: 4 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |
|
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| |
|
| 0.723 | 1.0 | 962 | 0.7320 | 0.6826 | 0.6766 | 0.6826 | 0.6703 | |
|
| 0.6337 | 2.0 | 1924 | 0.7344 | 0.6857 | 0.6847 | 0.6857 | 0.6852 | |
|
| 0.3821 | 3.0 | 2886 | 0.9051 | 0.6722 | 0.6885 | 0.6722 | 0.6759 | |
|
| 0.1811 | 4.0 | 3848 | 1.1789 | 0.6743 | 0.6787 | 0.6743 | 0.6759 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.47.0 |
|
- Pytorch 2.5.1+cu118 |
|
- Datasets 3.1.0 |
|
- Tokenizers 0.21.0 |
|
|
