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--- |
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license: apache-2.0 |
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library_name: peft |
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tags: |
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- parquet |
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- text-classification |
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datasets: |
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- tweet_eval |
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metrics: |
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- accuracy |
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base_model: Giyaseddin/distilroberta-base-finetuned-short-answer-assessment |
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model-index: |
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- name: Giyaseddin_distilroberta-base-finetuned-short-answer-assessment-finetuned-lora-tweet_eval_irony |
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results: |
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- task: |
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type: text-classification |
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name: Text Classification |
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dataset: |
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name: tweet_eval |
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type: tweet_eval |
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config: irony |
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split: validation |
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args: irony |
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metrics: |
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- type: accuracy |
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value: 0.7015706806282722 |
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name: accuracy |
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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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# Giyaseddin_distilroberta-base-finetuned-short-answer-assessment-finetuned-lora-tweet_eval_irony |
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This model is a fine-tuned version of [Giyaseddin/distilroberta-base-finetuned-short-answer-assessment](https://huggingface.co/Giyaseddin/distilroberta-base-finetuned-short-answer-assessment) on the tweet_eval dataset. |
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It achieves the following results on the evaluation set: |
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- accuracy: 0.7016 |
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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: 0.0005 |
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- train_batch_size: 32 |
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- eval_batch_size: 32 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 8 |
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### Training results |
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| accuracy | train_loss | epoch | |
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|:--------:|:----------:|:-----:| |
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| 0.4743 | None | 0 | |
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| 0.6188 | 0.7149 | 0 | |
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| 0.6262 | 0.6574 | 1 | |
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| 0.6660 | 0.6179 | 2 | |
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| 0.6932 | 0.5705 | 3 | |
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| 0.6859 | 0.5397 | 4 | |
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| 0.6796 | 0.5244 | 5 | |
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| 0.6953 | 0.5155 | 6 | |
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| 0.7016 | 0.4939 | 7 | |
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### Framework versions |
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- PEFT 0.8.2 |
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- Transformers 4.37.2 |
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- Pytorch 2.2.0 |
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- Datasets 2.16.1 |
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- Tokenizers 0.15.2 |
