distilbert-base-uncased-finetuned-hypertuned-ner
This model is a fine-tuned version of distilbert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.5683
- Precision: 0.3398
- Recall: 0.6481
- F1: 0.4459
- Accuracy: 0.8762
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: 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: 3
Training results
Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
---|---|---|---|---|---|---|---|
No log | 1.0 | 84 | 0.3566 | 0.2913 | 0.5556 | 0.3822 | 0.8585 |
No log | 2.0 | 168 | 0.4698 | 0.3366 | 0.6296 | 0.4387 | 0.8730 |
No log | 3.0 | 252 | 0.5683 | 0.3398 | 0.6481 | 0.4459 | 0.8762 |
Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0
- Downloads last month
- 3
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social
visibility and check back later, or deploy to Inference Endpoints (dedicated)
instead.