<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. -->
my_awsome_wnut_model
This model is a fine-tuned version of distilbert-base-uncased on the wnut_17 dataset. It achieves the following results on the evaluation set:
- Loss: 0.2858
- Precision: 0.4846
- Recall: 0.2632
- F1: 0.3411
- Accuracy: 0.9386
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: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
Training results
Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
---|---|---|---|---|---|---|---|
No log | 1.0 | 213 | 0.2976 | 0.3873 | 0.1974 | 0.2615 | 0.9352 |
No log | 2.0 | 426 | 0.2858 | 0.4846 | 0.2632 | 0.3411 | 0.9386 |
Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cpu
- Datasets 2.1.0
- Tokenizers 0.12.1