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distilbert-base-uncased-finetuned-ner
ssiyer/distilbert-base-uncased-finetuned-ner
distilbert-base-uncased-finetuned-ner is a token classification model from ssiyer. Use it when you need labels on individual words, such as names. It is set up for transformers. 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
- Task
- Token Classification
- Library
- transformers
- License
- apache-2.0
- Model type
- distilbert
- Access
- Public
- Created
- Nov 8, 2023
- Updated
- Nov 9, 2023
- SHA
- ade75c4b
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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: 0.0585
- Precision: 0.9276
- Recall: 0.9362
- F1: 0.9319
- Accuracy: 0.9837
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 |
|---|---|---|---|---|---|---|---|
| 0.247 | 1.0 | 878 | 0.0695 | 0.9028 | 0.9284 | 0.9154 | 0.9800 |
| 0.05 | 2.0 | 1756 | 0.0567 | 0.9266 | 0.9343 | 0.9304 | 0.9832 |
| 0.0307 | 3.0 | 2634 | 0.0585 | 0.9276 | 0.9362 | 0.9319 | 0.9837 |
Framework versions
- Transformers 4.35.0
- Pytorch 2.0.1
- Datasets 2.14.6
- Tokenizers 0.14.1
- Task
- Token Classification
- Library
- transformers
- Type
- distilbert
- License
- apache-2.0
- Created
- Nov 8, 2023
- Updated
- Nov 9, 2023