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distilbert-base-uncased-finetuned-ner
kinanmartin/distilbert-base-uncased-finetuned-ner
distilbert-base-uncased-finetuned-ner is a token classification model from kinanmartin. 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
- Jul 8, 2022
- Updated
- Jul 8, 2022
- SHA
- 21b8d303
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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 toydata dataset. It achieves the following results on the evaluation set:
- Loss: 0.1233
- Precision: 0.8373
- Recall: 0.8722
- F1: 0.8544
- Accuracy: 0.9640
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 |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 408 | 0.1435 | 0.7577 | 0.8557 | 0.8038 | 0.9526 |
| 0.1984 | 2.0 | 816 | 0.1246 | 0.8192 | 0.8747 | 0.8460 | 0.9620 |
| 0.0996 | 3.0 | 1224 | 0.1233 | 0.8373 | 0.8722 | 0.8544 | 0.9640 |
Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
- Task
- Token Classification
- Library
- transformers
- Type
- distilbert
- License
- apache-2.0
- Created
- Jul 8, 2022
- Updated
- Jul 8, 2022