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covid-tweets-sentiment-analysis-distilbert-model
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.5979
- Rmse: 0.6680
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: 3e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 10
Training results
Training Loss | Epoch | Step | Validation Loss | Rmse |
---|---|---|---|---|
0.7464 | 2.0 | 500 | 0.5979 | 0.6680 |
0.4318 | 4.0 | 1000 | 0.6374 | 0.6327 |
0.1694 | 6.0 | 1500 | 0.9439 | 0.6311 |
0.072 | 8.0 | 2000 | 1.1471 | 0.6556 |
0.0388 | 10.0 | 2500 | 1.2217 | 0.6437 |
Framework versions
- Transformers 4.29.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3