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Finished training.
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metadata
license: apache-2.0
library_name: peft
tags:
  - parquet
  - text-classification
datasets:
  - tweet_eval
metrics:
  - accuracy
base_model: Hate-speech-CNERG/bert-base-uncased-hatexplain-rationale-two
model-index:
  - name: >-
      Hate-speech-CNERG_bert-base-uncased-hatexplain-rationale-two-finetuned-lora-tweet_eval_irony
    results:
      - task:
          type: text-classification
          name: Text Classification
        dataset:
          name: tweet_eval
          type: tweet_eval
          config: irony
          split: validation
          args: irony
        metrics:
          - type: accuracy
            value: 0.6513089005235602
            name: accuracy

Hate-speech-CNERG_bert-base-uncased-hatexplain-rationale-two-finetuned-lora-tweet_eval_irony

This model is a fine-tuned version of Hate-speech-CNERG/bert-base-uncased-hatexplain-rationale-two on the tweet_eval dataset. It achieves the following results on the evaluation set:

  • accuracy: 0.6513

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: 0.0005
  • train_batch_size: 32
  • eval_batch_size: 32
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 8

Training results

accuracy train_loss epoch
0.4932 None 0
0.6105 0.6865 0
0.5979 0.6589 1
0.6136 0.6218 2
0.6115 0.5979 3
0.6272 0.5751 4
0.6283 0.5581 5
0.6366 0.5483 6
0.6513 0.5348 7

Framework versions

  • PEFT 0.8.2
  • Transformers 4.37.2
  • Pytorch 2.2.0
  • Datasets 2.16.1
  • Tokenizers 0.15.2