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samuellimabraz
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t5-small-qlora-finetune-tweetsumm

PEFT
Safetensors
Generated from Trainer
Eval Results
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Files changed (11) hide show
  1. README.md +96 -0
  2. adapter_config.json +29 -0
  3. adapter_model.safetensors +3 -0
  4. all_results.json +12 -0
  5. special_tokens_map.json +125 -0
  6. spiece.model +3 -0
  7. tokenizer.json +0 -0
  8. tokenizer_config.json +937 -0
  9. train_results.json +12 -0
  10. trainer_state.json +2400 -0
  11. training_args.bin +3 -0
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+ ---
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+ base_model: google-t5/t5-small
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+ datasets:
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+ - Andyrasika/TweetSumm-tuned
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+ library_name: peft
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+ license: apache-2.0
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+ metrics:
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+ - rouge
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+ - f1
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+ - precision
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+ - recall
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+ tags:
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+ - generated_from_trainer
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+ model-index:
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+ - name: t5-small-QLoRA-TweetSumm-1724713795
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+ results:
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+ - task:
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+ type: summarization
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+ name: Summarization
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+ dataset:
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+ name: Andyrasika/TweetSumm-tuned
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+ type: Andyrasika/TweetSumm-tuned
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+ metrics:
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+ - type: rouge
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+ value: 0.4298
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+ name: Rouge1
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+ - type: f1
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+ value: 0.887
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+ name: F1
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+ - type: precision
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+ value: 0.8838
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+ name: Precision
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+ - type: recall
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+ value: 0.8904
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+ name: Recall
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+ ---
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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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+
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+ # t5-small-QLoRA-TweetSumm-1724713795
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+
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+ This model is a fine-tuned version of [google-t5/t5-small](https://huggingface.co/google-t5/t5-small) on the Andyrasika/TweetSumm-tuned dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 2.0940
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+ - Rouge1: 0.4298
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+ - Rouge2: 0.1915
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+ - Rougel: 0.3559
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+ - Rougelsum: 0.3956
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+ - Gen Len: 47.8091
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+ - F1: 0.887
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+ - Precision: 0.8838
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+ - Recall: 0.8904
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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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: 8
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+ - eval_batch_size: 8
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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: 3
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+ - mixed_precision_training: Native AMP
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | F1 | Precision | Recall |
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+ |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|:------:|:---------:|:------:|
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+ | 2.3641 | 1.0 | 110 | 2.2019 | 0.4172 | 0.1774 | 0.3518 | 0.386 | 47.7636 | 0.8828 | 0.8806 | 0.8852 |
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+ | 2.2228 | 2.0 | 220 | 2.1040 | 0.419 | 0.1789 | 0.3477 | 0.3827 | 48.1182 | 0.8846 | 0.882 | 0.8875 |
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+ | 2.0174 | 3.0 | 330 | 2.0940 | 0.4298 | 0.1915 | 0.3559 | 0.3956 | 47.8091 | 0.887 | 0.8838 | 0.8904 |
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+
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+
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+ ### Framework versions
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+
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+ - PEFT 0.12.1.dev0
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+ - Transformers 4.44.0
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+ - Pytorch 2.4.0
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+ - Datasets 2.21.0
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+ - Tokenizers 0.19.1
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