PEFT
Safetensors
Transformers
lora
Eval Results (legacy)
Dikshant182004 commited on
Commit
8647962
·
verified ·
1 Parent(s): 33bdb6d

End of training

Browse files
README.md ADDED
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+ ---
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+ library_name: peft
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+ license: apache-2.0
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+ base_model: google-t5/t5-base
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+ tags:
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+ - base_model:adapter:google-t5/t5-base
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+ - lora
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+ - transformers
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+ datasets:
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+ - Andyrasika/TweetSumm-tuned
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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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+ model-index:
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+ - name: t5-base-lora-finetune-tweetsumm-1759930365
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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: 0.4392
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+ name: Rouge1
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+ - type: f1
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+ value: 0.8888
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+ name: F1
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+ - type: precision
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+ value: 0.8854
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+ name: Precision
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+ - type: recall
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+ value: 0.8924
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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-base-lora-finetune-tweetsumm-1759930365
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+
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+ This model is a fine-tuned version of [google-t5/t5-base](https://huggingface.co/google-t5/t5-base) on the Andyrasika/TweetSumm-tuned dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 1.8115
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+ - Rouge1: 0.4392
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+ - Rouge2: 0.2021
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+ - Rougel: 0.3685
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+ - Rougelsum: 0.4054
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+ - Gen Len: 47.8545
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+ - F1: 0.8888
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+ - Precision: 0.8854
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+ - Recall: 0.8924
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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: 0.001
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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: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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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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+ | 2.1023 | 1.0 | 110 | 1.8780 | 0.423 | 0.1965 | 0.3626 | 0.3937 | 48.7 | 0.8843 | 0.8815 | 0.8873 |
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+ | 1.7351 | 2.0 | 220 | 1.8109 | 0.4492 | 0.2113 | 0.3799 | 0.4137 | 49.1909 | 0.8907 | 0.8879 | 0.8937 |
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+ | 1.5234 | 3.0 | 330 | 1.8115 | 0.4392 | 0.2021 | 0.3685 | 0.4054 | 47.8545 | 0.8888 | 0.8854 | 0.8924 |
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+
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+
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+ ### Framework versions
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+
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+ - PEFT 0.17.1
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+ - Transformers 4.56.2
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+ - Pytorch 2.8.0+cu126
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+ - Datasets 4.0.0
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+ - Tokenizers 0.22.1
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