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update model card README.md

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+ ---
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+ license: apache-2.0
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+ tags:
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+ - summarization
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+ - fa
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+ - mt5
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+ - Abstractive Summarization
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+ - generated_from_trainer
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+ datasets:
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+ - pn_summary
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+ model-index:
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+ - name: mt5-base-finetuned-fa
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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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+ # mt5-base-finetuned-fa
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+
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+ This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on the pn_summary dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 2.6477
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+ - Rouge-1: 33.7
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+ - Rouge-2: 21.28
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+ - Rouge-l: 31.69
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+ - Gen Len: 19.0
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+ - Bertscore: 74.52
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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: 4
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+ - eval_batch_size: 4
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+ - seed: 42
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+ - gradient_accumulation_steps: 8
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+ - total_train_batch_size: 32
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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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+ - lr_scheduler_warmup_steps: 250
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+ - num_epochs: 5
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+ - label_smoothing_factor: 0.1
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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-1 | Rouge-2 | Rouge-l | Gen Len | Bertscore |
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+ |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:-------:|:---------:|
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+ | 3.3828 | 1.0 | 1875 | 2.8114 | 32.17 | 19.47 | 30.12 | 18.99 | 74.25 |
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+ | 2.8204 | 2.0 | 3750 | 2.7080 | 32.67 | 19.92 | 30.56 | 19.0 | 74.31 |
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+ | 2.6907 | 3.0 | 5625 | 2.6724 | 33.22 | 20.44 | 31.11 | 19.0 | 74.47 |
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+ | 2.6029 | 4.0 | 7500 | 2.6513 | 33.46 | 20.75 | 31.38 | 19.0 | 74.54 |
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+ | 2.5414 | 5.0 | 9375 | 2.6477 | 33.68 | 20.91 | 31.62 | 19.0 | 74.58 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.19.4
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+ - Pytorch 1.11.0+cu113
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+ - Datasets 2.2.2
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+ - Tokenizers 0.12.1