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sentiment-roberta-clean-e8-b16-data2
This model is a fine-tuned version of Amalq/autotrain-smm4h_large_roberta_clean-874027878 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.9974
- F1: 0.7709
- Recall: 0.7709
- Precision: 0.7709
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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
Training results
Training Loss | Epoch | Step | Validation Loss | F1 | Recall | Precision |
---|---|---|---|---|---|---|
No log | 1.0 | 375 | 0.7813 | 0.7412 | 0.7412 | 0.7412 |
0.5973 | 2.0 | 750 | 0.7826 | 0.7332 | 0.7332 | 0.7332 |
0.2098 | 3.0 | 1125 | 1.1970 | 0.7547 | 0.7547 | 0.7547 |
0.1231 | 4.0 | 1500 | 1.5843 | 0.7520 | 0.7520 | 0.7520 |
0.1231 | 5.0 | 1875 | 1.7089 | 0.7520 | 0.7520 | 0.7520 |
0.0457 | 6.0 | 2250 | 1.7845 | 0.7601 | 0.7601 | 0.7601 |
0.0228 | 7.0 | 2625 | 2.0039 | 0.7601 | 0.7601 | 0.7601 |
0.0134 | 8.0 | 3000 | 1.9974 | 0.7709 | 0.7709 | 0.7709 |
Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3