distilbert-base-uncased-ner-mit-restaurant
This model is a fine-tuned version of distilbert-base-uncased on the mit_restaurant dataset. It achieves the following results on the evaluation set:
- Loss: 0.3097
- Precision: 0.7874
- Recall: 0.8104
- F1: 0.7988
- Accuracy: 0.9119
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: 16
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 4
Training results
Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
---|---|---|---|---|---|---|---|
No log | 1.0 | 431 | 0.4575 | 0.6220 | 0.6856 | 0.6523 | 0.8650 |
1.1705 | 2.0 | 862 | 0.3183 | 0.7747 | 0.7953 | 0.7848 | 0.9071 |
0.3254 | 3.0 | 1293 | 0.3163 | 0.7668 | 0.8021 | 0.7841 | 0.9058 |
0.2287 | 4.0 | 1724 | 0.3097 | 0.7874 | 0.8104 | 0.7988 | 0.9119 |
Framework versions
- Transformers 4.8.2
- Pytorch 1.8.1+cu111
- Datasets 1.8.0
- Tokenizers 0.10.3
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