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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-05-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-05-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: 3.3957
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+ - Rouge/rouge1: 0.4646
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+ - Rouge/rouge2: 0.2089
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+ - Rouge/rougel: 0.3939
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+ - Rouge/rougelsum: 0.3945
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+ - Bertscore/bertscore-precision: 0.8956
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+ - Bertscore/bertscore-recall: 0.8935
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+ - Bertscore/bertscore-f1: 0.8944
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+ - Meteor: 0.4132
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+ - Gen Len: 37.5
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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-05
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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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+ | 0.6107 | 1.0 | 109 | 2.4784 | 0.449 | 0.1974 | 0.3774 | 0.3776 | 0.8943 | 0.8904 | 0.8922 | 0.3981 | 36.9182 |
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+ | 0.3993 | 2.0 | 218 | 2.7984 | 0.4656 | 0.2145 | 0.3954 | 0.3965 | 0.8975 | 0.8914 | 0.8943 | 0.408 | 35.1364 |
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+ | 0.2779 | 3.0 | 327 | 3.0563 | 0.4669 | 0.2112 | 0.3981 | 0.3995 | 0.8961 | 0.8905 | 0.8931 | 0.4088 | 36.0545 |
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+ | 0.2038 | 4.0 | 436 | 3.2410 | 0.4639 | 0.2052 | 0.3895 | 0.3904 | 0.896 | 0.8949 | 0.8953 | 0.4109 | 37.9 |
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+ | 0.1606 | 5.0 | 545 | 3.3263 | 0.4582 | 0.2063 | 0.391 | 0.392 | 0.8961 | 0.893 | 0.8944 | 0.4033 | 36.5545 |
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+ | 0.1282 | 6.0 | 654 | 3.3957 | 0.4646 | 0.2089 | 0.3939 | 0.3945 | 0.8956 | 0.8935 | 0.8944 | 0.4132 | 37.5 |
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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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