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--- |
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license: mit |
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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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- precision |
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- recall |
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- f1 |
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model-index: |
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- name: roberta-base-finetuned-3d-sentiment |
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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-base-finetuned-3d-sentiment |
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This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5817 |
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- Accuracy: 0.7753 |
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- Precision: 0.7757 |
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- Recall: 0.7753 |
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- F1: 0.7745 |
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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: 2e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 64 |
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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: 6381 |
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- num_epochs: 7 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |
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|:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------:|:------:|:------:| |
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| 0.7758 | 1.0 | 1595 | 0.7691 | 0.7069 | 0.7256 | 0.7069 | 0.7052 | |
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| 0.5496 | 2.0 | 3190 | 0.6961 | 0.7255 | 0.7441 | 0.7255 | 0.7252 | |
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| 0.4856 | 3.0 | 4785 | 0.6451 | 0.7368 | 0.7562 | 0.7368 | 0.7328 | |
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| 0.4257 | 4.0 | 6380 | 0.5817 | 0.7753 | 0.7757 | 0.7753 | 0.7745 | |
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| 0.351 | 5.0 | 7975 | 0.6637 | 0.7633 | 0.7717 | 0.7633 | 0.7637 | |
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| 0.2551 | 6.0 | 9570 | 0.7646 | 0.7696 | 0.7738 | 0.7696 | 0.7699 | |
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| 0.1845 | 7.0 | 11165 | 0.8529 | 0.7674 | 0.7730 | 0.7674 | 0.7680 | |
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### Framework versions |
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- Transformers 4.26.1 |
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- Pytorch 1.13.1+cu117 |
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- Datasets 2.10.1 |
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- Tokenizers 0.13.3 |
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