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End of training

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README.md ADDED
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+ ---
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+ library_name: transformers
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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-2409-1947-lr-3e-06-bs-4-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-2409-1947-lr-3e-06-bs-4-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: 7.3666
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+ - Rouge/rouge1: 0.2722
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+ - Rouge/rouge2: 0.0714
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+ - Rouge/rougel: 0.2029
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+ - Rouge/rougelsum: 0.2031
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+ - Bertscore/bertscore-precision: 0.8612
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+ - Bertscore/bertscore-recall: 0.8618
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+ - Bertscore/bertscore-f1: 0.8615
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+ - Meteor: 0.2582
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+ - Gen Len: 44.0
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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: 4
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+ - eval_batch_size: 4
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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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+ | 0.2442 | 1.0 | 217 | 7.1796 | 0.2722 | 0.0714 | 0.2029 | 0.2031 | 0.8612 | 0.8618 | 0.8615 | 0.2582 | 44.0 |
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+ | 0.2514 | 2.0 | 434 | 7.2470 | 0.2722 | 0.0714 | 0.2029 | 0.2031 | 0.8612 | 0.8618 | 0.8615 | 0.2582 | 44.0 |
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+ | 0.2226 | 3.0 | 651 | 7.2953 | 0.2722 | 0.0714 | 0.2029 | 0.2031 | 0.8612 | 0.8618 | 0.8615 | 0.2582 | 44.0 |
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+ | 0.2207 | 4.0 | 868 | 7.3342 | 0.2722 | 0.0714 | 0.2029 | 0.2031 | 0.8612 | 0.8618 | 0.8615 | 0.2582 | 44.0 |
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+ | 0.2177 | 5.0 | 1085 | 7.3588 | 0.2722 | 0.0714 | 0.2029 | 0.2031 | 0.8612 | 0.8618 | 0.8615 | 0.2582 | 44.0 |
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+ | 0.2176 | 6.0 | 1302 | 7.3666 | 0.2722 | 0.0714 | 0.2029 | 0.2031 | 0.8612 | 0.8618 | 0.8615 | 0.2582 | 44.0 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.44.2
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+ - Pytorch 2.4.0
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+ - Datasets 3.0.0
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+ - Tokenizers 0.19.1
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