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SA-tweet-bert-large-e6-w1-1.5
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: 0.9227
- Accuracy: 0.9066
- F1: 0.8688
- Precision: 0.9115
- Recall: 0.8299
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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6
Training results
Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
---|---|---|---|---|---|---|---|
0.4119 | 1.0 | 2321 | 0.6382 | 0.8943 | 0.8601 | 0.8488 | 0.8716 |
0.2178 | 2.0 | 4642 | 0.6811 | 0.8888 | 0.8571 | 0.8219 | 0.8955 |
0.1101 | 3.0 | 6963 | 0.7734 | 0.8910 | 0.8604 | 0.8229 | 0.9015 |
0.0234 | 4.0 | 9284 | 0.7485 | 0.9066 | 0.8704 | 0.9010 | 0.8418 |
0.0333 | 5.0 | 11605 | 0.7888 | 0.9099 | 0.8796 | 0.8757 | 0.8836 |
0.0093 | 6.0 | 13926 | 0.9227 | 0.9066 | 0.8688 | 0.9115 | 0.8299 |
Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3