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---
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
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