my_dist_new_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: 0.3017
- Precision: 0.5340
- Recall: 0.2039
- F1: 0.2951
- Accuracy: 0.9364
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
- 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 | 107 | 0.3192 | 0.3862 | 0.0519 | 0.0915 | 0.9298 |
No log | 2.0 | 214 | 0.3017 | 0.5340 | 0.2039 | 0.2951 | 0.9364 |
Framework versions
- Transformers 4.41.2
- Pytorch 2.1.2
- Datasets 2.19.2
- Tokenizers 0.19.1
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Finetuned from
Dataset used to train bhadauriaupendra062/my_dist_new_model
Evaluation results
- Precision on wnut_17test set self-reported0.534
- Recall on wnut_17test set self-reported0.204
- F1 on wnut_17test set self-reported0.295
- Accuracy on wnut_17test set self-reported0.936