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This model is a fine-tuned version of distilbert/distilroberta-base on the None dataset. It achieves the following results on the evaluation set: Loss: 1.2255 F1 Micro: 0.7950 F1 Macro: 0.7537 Precision Micro: 0.8288...
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This model is a fine-tuned version of distilbert/distilroberta-base on the None dataset. It achieves the following results on the evaluation set:
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The following hyperparameters were used during training:
| Training Loss | Epoch | Step | Validation Loss | F1 Micro | F1 Macro | Precision Micro | Recall Micro |
|---|---|---|---|---|---|---|---|
| 1.2428 | 1.0 | 10479 | 1.2420 | 0.7684 | 0.7237 | 0.8284 | 0.7166 |
| 1.2363 | 2.0 | 20958 | 1.2316 | 0.7920 | 0.7553 | 0.8247 | 0.7618 |
| 1.2324 | 3.0 | 31437 | 1.2279 | 0.7979 | 0.7646 | 0.8306 | 0.7677 |
--- library_name: transformers license: apache-2.0 base_model: distilbert/distilroberta-base tags: - generated_from_trainer model-index: - name: distilroberta-8-4-1.25-85-65-75 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilroberta-8-4-1.25-85-65-75 This model is a fine-tuned version of [distilbert/distilroberta-base](https://huggingface.co/distilbert/distilroberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.2255 - F1 Micro: 0.7950 - F1 Macro: 0.7537 - Precision Micro: 0.8288 - Recall Micro: 0.7638 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 3143 - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 Micro | F1 Macro | Precision Micro | Recall Micro | |:-------------:|:-----:|:-----:|:---------------:|:---------:|:---------:|:----------------:|:-------------:| | 1.2428 | 1.0 | 10479 | 1.2420 | 0.7684 | 0.7237 | 0.8284 | 0.7166 | | 1.2363 | 2.0 | 20958 | 1.2316 | 0.7920 | 0.7553 | 0.8247 | 0.7618 | | 1.2324 | 3.0 | 31437 | 1.2279 | 0.7979 | 0.7646 | 0.8306 | 0.7677 | ### Framework versions - Transformers 5.10.2 - Pytorch 2.11.0+cu128 - Datasets 4.8.5 - Tokenizers 0.22.2
Source context: 11 downloads · 0 likes · Pipeline text-classification · Library transformers · Repo emotions-entailment/distilroberta-8-4-1.25-85-65-75
Source context: 5 downloads · 0 likes · Pipeline text-classification · Library transformers · Repo emotions-entailment/distilroberta-8-4-1.25-85-65-75
