---
library_name: peft
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-lora-hatexplain
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# finetuned-distilbert-lora-hatexplain

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: 0.7335
- Accuracy: 0.6767
- Precision: 0.6679
- Recall: 0.6767
- F1: 0.6703

## 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: 0.0001
- 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.7301        | 1.0   | 962  | 0.7683          | 0.6561   | 0.6475    | 0.6561 | 0.6446 |
| 0.747         | 2.0   | 1924 | 0.7493          | 0.6644   | 0.6611    | 0.6644 | 0.6597 |
| 0.7918        | 3.0   | 2886 | 0.7332          | 0.6774   | 0.6712    | 0.6774 | 0.6710 |
| 0.6507        | 4.0   | 3848 | 0.7357          | 0.6805   | 0.6735    | 0.6805 | 0.6747 |


### Framework versions

- PEFT 0.14.0
- Transformers 4.47.0
- Pytorch 2.5.1+cu118
- Datasets 3.1.0
- Tokenizers 0.21.0