t5_small_autotagging

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t5_small_autotagging

This model is a fine-tuned version of RevoltronTechno/t5_small_context_tagging on the autotagging dataset. It achieves the following results on the evaluation set:

• Loss: 0.5257

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: 2e-05
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• num_epochs: 50

Training results

Framework versions

• Transformers 4.46.2
• Pytorch 2.5.1+cu121
• Datasets 3.1.0
• Tokenizers 0.20.3

Evaluation Metrics

The model was evaluated using the following metrics:

Metric Descriptions:

• ROUGE-1: Measures the overlap of unigrams between the generated text and reference text.
• ROUGE-2: Measures the overlap of bigrams between the generated text and reference text.
• ROUGE-L: Measures the longest common subsequence between the generated text and reference text.
• BLEU Score: Measures the precision of n-grams in the generated text compared to the reference text.