Domain-specific continued pretraining of RoBERTa
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README.md
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---
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library_name: transformers
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license: mit
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base_model: roberta-base
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tags:
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- generated_from_trainer
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model-index:
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- name: roberta-continued-pretraining
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results: []
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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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# roberta-continued-pretraining
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This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 1.2371
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 5e-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: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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- lr_scheduler_type: linear
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- lr_scheduler_warmup_steps: 500
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- num_epochs: 3
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### Training results
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| Training Loss | Epoch | Step | Validation Loss |
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|:-------------:|:------:|:----:|:---------------:|
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| 1.6688 | 0.3337 | 1000 | 1.4834 |
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| 1.5534 | 0.6673 | 2000 | 1.4207 |
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| 1.5071 | 1.0010 | 3000 | 1.3937 |
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| 1.4337 | 1.3347 | 4000 | 1.3301 |
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| 1.4162 | 1.6683 | 5000 | 1.3126 |
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| 1.372 | 2.0020 | 6000 | 1.2803 |
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| 1.3325 | 2.3357 | 7000 | 1.2564 |
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| 1.307 | 2.6693 | 8000 | 1.2371 |
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### Framework versions
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- Transformers 4.48.1
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- Pytorch 2.5.1+cu121
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- Datasets 3.2.0
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- Tokenizers 0.21.0
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