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m-aliabbas1
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roberta_en_med_merged_classes

Text Classification
Transformers
Safetensors
roberta
Generated from Trainer
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README.md ADDED
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+ ---
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+ library_name: transformers
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+ license: mit
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+ base_model: FacebookAI/roberta-base
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+ tags:
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+ - generated_from_trainer
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+ metrics:
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+ - accuracy
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+ model-index:
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+ - name: roberta_en_med_merged_classes
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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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+ # roberta_en_med_merged_classes
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+
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+ This model is a fine-tuned version of [FacebookAI/roberta-base](https://huggingface.co/FacebookAI/roberta-base) on the None dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.4635
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+ - Accuracy: 0.8405
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+ - F1 Macro: 0.8001
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+ - F1 Weighted: 0.8401
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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: 2e-05
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+ - train_batch_size: 32
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+ - eval_batch_size: 64
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+ - seed: 42
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+ - gradient_accumulation_steps: 2
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+ - total_train_batch_size: 64
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+ - optimizer: Use adamw_torch_fused 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_ratio: 0.1
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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 | Accuracy | F1 Macro | F1 Weighted |
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+ |:-------------:|:------:|:----:|:---------------:|:--------:|:--------:|:-----------:|
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+ | 0.7259 | 0.4681 | 400 | 0.6577 | 0.7766 | 0.6330 | 0.7683 |
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+ | 0.5302 | 0.9362 | 800 | 0.5115 | 0.8099 | 0.7577 | 0.8083 |
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+ | 0.4494 | 1.4037 | 1200 | 0.4656 | 0.8280 | 0.7818 | 0.8278 |
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+ | 0.4417 | 1.8719 | 1600 | 0.4583 | 0.8334 | 0.7872 | 0.8326 |
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+ | 0.3551 | 2.3394 | 2000 | 0.4665 | 0.8239 | 0.7727 | 0.8238 |
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+ | 0.3486 | 2.8075 | 2400 | 0.4337 | 0.8390 | 0.7933 | 0.8372 |
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+ | 0.3028 | 3.2750 | 2800 | 0.4387 | 0.8391 | 0.7961 | 0.8392 |
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+ | 0.2975 | 3.7431 | 3200 | 0.4378 | 0.8407 | 0.7986 | 0.8400 |
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+ | 0.2576 | 4.2106 | 3600 | 0.4655 | 0.8359 | 0.7975 | 0.8358 |
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+ | 0.2602 | 4.6788 | 4000 | 0.4580 | 0.8374 | 0.7960 | 0.8377 |
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+ | 0.2196 | 5.1463 | 4400 | 0.4635 | 0.8405 | 0.8001 | 0.8401 |
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+ | 0.2153 | 5.6144 | 4800 | 0.4738 | 0.8388 | 0.7961 | 0.8385 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.56.1
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+ - Pytorch 2.8.0+cu126
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+ - Datasets 4.1.0
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+ - Tokenizers 0.22.0
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