distilbert_finetuned_claimdecomp
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: 9.3205
- Accuracy: 0.335
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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 30000
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.0064 | 50.0 | 5000 | 5.7963 | 0.375 |
| 0.0 | 100.0 | 10000 | 7.2917 | 0.36 |
| 0.0 | 150.0 | 15000 | 7.0473 | 0.33 |
| 0.0 | 200.0 | 20000 | 8.0988 | 0.31 |
| 0.0 | 250.0 | 25000 | 8.8824 | 0.325 |
| 0.0 | 300.0 | 30000 | 9.3205 | 0.335 |
Framework versions
- Transformers 4.34.1
- Pytorch 2.0.0
- Datasets 2.14.5
- Tokenizers 0.14.1
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Model tree for gavulsim/distilbert_finetuned_claimdecomp
Base model
distilbert/distilbert-base-uncased