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README.md ADDED
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+ ---
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+ tags:
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+ - generated_from_trainer
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+ datasets:
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+ - xsum
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+ model-index:
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+ - name: RoFormer_small-summarization
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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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+ # RoFormer_small-summarization
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+
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+ This model is a fine-tuned version of [](https://huggingface.co/) on the xsum dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 4.3373
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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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+ - gradient_accumulation_steps: 4
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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: cosine
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+ - lr_scheduler_warmup_steps: 100
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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 |
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+ |:-------------:|:-----:|:-----:|:---------------:|
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+ | 4.975 | 1.0 | 5762 | 4.4897 |
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+ | 4.4149 | 2.0 | 11525 | 4.3647 |
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+ | 4.3296 | 3.0 | 17286 | 4.3373 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.35.2
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+ - Pytorch 2.1.0+cu118
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+ - Datasets 2.15.0
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+ - Tokenizers 0.15.0
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+ "architectures": [
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+ "RoFormerForCausalLM"
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+ ],
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+ }
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