microsoft-deberta-v3-large_ner_wnut_17
This model is a fine-tuned version of microsoft/deberta-v3-large on the wnut_17 dataset. It achieves the following results on the evaluation set:
- Loss: 0.2199
- Precision: 0.7671
- Recall: 0.6184
- F1: 0.6848
- Accuracy: 0.9667
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: cosine
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 213 | 0.1751 | 0.6884 | 0.5682 | 0.6225 | 0.9601 |
| No log | 2.0 | 426 | 0.1702 | 0.7351 | 0.6208 | 0.6732 | 0.9655 |
| 0.1003 | 3.0 | 639 | 0.1954 | 0.7360 | 0.6136 | 0.6693 | 0.9656 |
| 0.1003 | 4.0 | 852 | 0.2113 | 0.7595 | 0.6232 | 0.6846 | 0.9669 |
| 0.015 | 5.0 | 1065 | 0.2199 | 0.7671 | 0.6184 | 0.6848 | 0.9667 |
Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
- Downloads last month
- 7
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.
Model tree for Gladiator/microsoft-deberta-v3-large_ner_wnut_17
Dataset used to train Gladiator/microsoft-deberta-v3-large_ner_wnut_17
Space using Gladiator/microsoft-deberta-v3-large_ner_wnut_17 1
Evaluation results
- Precision on wnut_17self-reported0.767
- Recall on wnut_17self-reported0.618
- F1 on wnut_17self-reported0.685
- Accuracy on wnut_17self-reported0.967