metadata
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: distilbert-hatexplain
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: 0.6990644490644491
- name: Precision
type: precision
value: 0.6974890380019948
- name: Recall
type: recall
value: 0.6990644490644491
- name: F1
type: f1
value: 0.6978790945993021
distilbert-hatexplain
This model is a fine-tuned version of distilbert-base-uncased on the hatexplain dataset. It achieves the following results on the evaluation set:
- Loss: 0.8165
- Accuracy: 0.6991
- Precision: 0.6975
- Recall: 0.6991
- F1: 0.6979
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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use 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: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|---|---|---|---|---|---|---|---|
| 0.6131 | 1.0 | 1923 | 0.7399 | 0.6925 | 0.6877 | 0.6925 | 0.6847 |
| 0.7386 | 2.0 | 3846 | 0.7254 | 0.7040 | 0.7033 | 0.7040 | 0.7036 |
| 0.6471 | 3.0 | 5769 | 0.8259 | 0.7019 | 0.6995 | 0.7019 | 0.7005 |
Framework versions
- Transformers 4.47.0
- Pytorch 2.5.1+cu118
- Datasets 3.1.0
- Tokenizers 0.21.0