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Finished training.
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metadata
library_name: peft
tags:
  - parquet
  - text-classification
datasets:
  - tweet_eval
metrics:
  - accuracy
base_model: jb2k/bert-base-multilingual-cased-language-detection
model-index:
  - name: >-
      jb2k_bert-base-multilingual-cased-language-detection-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: 0.5905759162303665
            name: accuracy

jb2k_bert-base-multilingual-cased-language-detection-finetuned-lora-tweet_eval_irony

This model is a fine-tuned version of jb2k/bert-base-multilingual-cased-language-detection on the tweet_eval dataset. It achieves the following results on the evaluation set:

  • accuracy: 0.5906

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
0.4806 None 0
0.5476 0.6896 0
0.5654 0.6710 1
0.5738 0.6549 2
0.5770 0.6416 3
0.5801 0.6325 4
0.5853 0.6288 5
0.5853 0.6242 6
0.5906 0.6154 7

Framework versions

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