--- library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: manueltonneau/bert-twitter-en-is-hired model-index: - name: manueltonneau_bert-twitter-en-is-hired-finetuned-lora-tweet_eval_hate results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: hate split: validation args: hate metrics: - type: accuracy value: 0.73 name: accuracy --- # manueltonneau_bert-twitter-en-is-hired-finetuned-lora-tweet_eval_hate This model is a fine-tuned version of [manueltonneau/bert-twitter-en-is-hired](https://huggingface.co/manueltonneau/bert-twitter-en-is-hired) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.73 ## 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: 0.0004 - 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: 4 ### Training results | accuracy | train_loss | epoch | |:--------:|:----------:|:-----:| | 0.573 | None | 0 | | 0.68 | 0.7226 | 0 | | 0.711 | 0.5114 | 1 | | 0.711 | 0.4613 | 2 | | 0.73 | 0.4372 | 3 | ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2