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End of training

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README.md ADDED
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+ ---
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+ license: apache-2.0
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+ base_model: sshleifer/distilbart-xsum-12-6
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+ tags:
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+ - generated_from_trainer
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+ model-index:
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+ - name: bart-abs-1509-0313-lr-3e-06-bs-8-maxep-6
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+ results: []
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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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+ # bart-abs-1509-0313-lr-3e-06-bs-8-maxep-6
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+
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+ This model is a fine-tuned version of [sshleifer/distilbart-xsum-12-6](https://huggingface.co/sshleifer/distilbart-xsum-12-6) on the None dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 6.6809
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+ - Rouge/rouge1: 0.3111
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+ - Rouge/rouge2: 0.0793
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+ - Rouge/rougel: 0.2212
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+ - Rouge/rougelsum: 0.2213
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+ - Bertscore/bertscore-precision: 0.8659
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+ - Bertscore/bertscore-recall: 0.864
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+ - Bertscore/bertscore-f1: 0.8649
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+ - Meteor: 0.228
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+ - Gen Len: 36.0
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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: 3e-06
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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: 6
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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 | Rouge/rouge1 | Rouge/rouge2 | Rouge/rougel | Rouge/rougelsum | Bertscore/bertscore-precision | Bertscore/bertscore-recall | Bertscore/bertscore-f1 | Meteor | Gen Len |
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+ |:-------------:|:-----:|:----:|:---------------:|:------------:|:------------:|:------------:|:---------------:|:-----------------------------:|:--------------------------:|:----------------------:|:------:|:-------:|
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+ | 0.2815 | 1.0 | 109 | 6.6245 | 0.323 | 0.081 | 0.2516 | 0.2521 | 0.8764 | 0.8641 | 0.8702 | 0.2583 | 36.0 |
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+ | 0.3942 | 2.0 | 218 | 6.6890 | 0.2515 | 0.0697 | 0.2094 | 0.21 | 0.8374 | 0.8631 | 0.85 | 0.2474 | 48.0 |
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+ | 0.3862 | 3.0 | 327 | 6.6692 | 0.3111 | 0.0793 | 0.2212 | 0.2213 | 0.8659 | 0.864 | 0.8649 | 0.228 | 36.0 |
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+ | 0.3708 | 4.0 | 436 | 6.6624 | 0.2439 | 0.0504 | 0.2065 | 0.2067 | 0.8544 | 0.8581 | 0.8562 | 0.229 | 45.0 |
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+ | 0.3679 | 5.0 | 545 | 6.6795 | 0.3111 | 0.0793 | 0.2212 | 0.2213 | 0.8659 | 0.864 | 0.8649 | 0.228 | 36.0 |
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+ | 0.3629 | 6.0 | 654 | 6.6809 | 0.3111 | 0.0793 | 0.2212 | 0.2213 | 0.8659 | 0.864 | 0.8649 | 0.228 | 36.0 |
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+
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+
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+ ### Framework versions
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+
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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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