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README.md
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---
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library_name: peft
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license: apache-2.0
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base_model: distilbert-base-uncased
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tags:
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- generated_from_trainer
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datasets:
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- hatexplain
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metrics:
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- accuracy
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- precision
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- recall
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- f1
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model-index:
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- name: finetuned-distilbert-lora-hatexplain
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results: []
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# finetuned-distilbert-lora-hatexplain
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This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the hatexplain dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.7335
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- Accuracy: 0.6767
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- Precision: 0.6679
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- Recall: 0.6767
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- F1: 0.6703
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 0.0001
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- train_batch_size: 16
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- eval_batch_size: 16
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- seed: 42
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- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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- lr_scheduler_type: linear
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- num_epochs: 4
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
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| 0.7301 | 1.0 | 962 | 0.7683 | 0.6561 | 0.6475 | 0.6561 | 0.6446 |
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| 0.747 | 2.0 | 1924 | 0.7493 | 0.6644 | 0.6611 | 0.6644 | 0.6597 |
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| 0.7918 | 3.0 | 2886 | 0.7332 | 0.6774 | 0.6712 | 0.6774 | 0.6710 |
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| 0.6507 | 4.0 | 3848 | 0.7357 | 0.6805 | 0.6735 | 0.6805 | 0.6747 |
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### Framework versions
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- PEFT 0.14.0
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- Transformers 4.47.0
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- Pytorch 2.5.1+cu118
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- Datasets 3.1.0
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- Tokenizers 0.21.0
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