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distilbert-base-uncased-imdb
This model is a fine-tuned version of distilbert-base-uncased on an imdb dataset where an evaluation of 5000 samples was created by splitting the training set. It achieves the following results on the evaluation set:
- Loss: 0.6252
- Accuracy: 0.9214
Model description
More information needed
Intended uses & limitations
This model was trained for the introduction to Natural language processing course of EPITA.
Training and evaluation data
The training/evaluation split was generated using a seed of 42 and a test_size of 0.2.
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 128
- seed: 1337
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.2875 | 1.0 | 625 | 0.2286 | 0.9102 |
| 0.1685 | 2.0 | 1250 | 0.2416 | 0.9128 |
| 0.1171 | 3.0 | 1875 | 0.3223 | 0.917 |
| 0.0493 | 4.0 | 2500 | 0.3667 | 0.9162 |
| 0.023 | 5.0 | 3125 | 0.4074 | 0.92 |
| 0.015 | 6.0 | 3750 | 0.4291 | 0.9236 |
| 0.0129 | 7.0 | 4375 | 0.5452 | 0.9194 |
| 0.0051 | 8.0 | 5000 | 0.5886 | 0.9146 |
| 0.0027 | 9.0 | 5625 | 0.6310 | 0.9186 |
| 0.002 | 10.0 | 6250 | 0.6252 | 0.9214 |
Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
