sentiment-analysis
This model is a fine-tuned version of distilbert/distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.0940
- Accuracy: 0.586
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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
Training results
Training Loss | Epoch | Step | Validation Loss | Accuracy |
---|---|---|---|---|
No log | 0.4 | 50 | 1.5942 | 0.401 |
No log | 0.8 | 100 | 1.5160 | 0.4765 |
No log | 1.2 | 150 | 1.3189 | 0.535 |
No log | 1.6 | 200 | 1.2154 | 0.551 |
No log | 2.0 | 250 | 1.1434 | 0.562 |
No log | 2.4 | 300 | 1.1106 | 0.575 |
No log | 2.8 | 350 | 1.0940 | 0.586 |
Framework versions
- PEFT 0.11.1
- Transformers 4.41.2
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
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