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distilbert-base-uncased-finetuned-ner
jwu205/distilbert-base-uncased-finetuned-ner
distilbert-base-uncased-finetuned-ner is a machine learning model from jwu205. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as apache-2.0.
should probably proofread and complete it, then remove this comment. --
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At a glance
- License
- apache-2.0
- Model type
- distilbert
- Access
- Public
- Created
- Sep 18, 2024
- Updated
- Sep 19, 2024
- SHA
- 47070d4f
Base models
Datasets
Model card
From the Hugging Face model README
<!-- 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. -->
distilbert-base-uncased-finetuned-ner
This model is a fine-tuned version of distilbert-base-uncased on the conll2003 dataset. It achieves the following results on the evaluation set:
- Loss: 0.0664
- Precision: 0.9306
- Recall: 0.9393
- F1: 0.9349
- Accuracy: 0.9842
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: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.0525 | 1.0 | 878 | 0.0671 | 0.9121 | 0.9308 | 0.9213 | 0.9820 |
| 0.0287 | 2.0 | 1756 | 0.0640 | 0.9281 | 0.9361 | 0.9321 | 0.9838 |
| 0.0169 | 3.0 | 2634 | 0.0664 | 0.9306 | 0.9393 | 0.9349 | 0.9842 |
Framework versions
- Transformers 4.32.1
- Pytorch 2.2.1+cpu
- Datasets 2.21.0
- Tokenizers 0.13.2
- Type
- distilbert
- License
- apache-2.0
- Created
- Sep 18, 2024
- Updated
- Sep 19, 2024