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distilbert-base-uncased-finetuned-ner
emilyblah/distilbert-base-uncased-finetuned-ner
distilbert-base-uncased-finetuned-ner is a token classification model from emilyblah. 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
- Dec 5, 2023
- Updated
- Dec 6, 2023
- SHA
- 051ded74
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Base models
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 None dataset. It achieves the following results on the evaluation set:
- Loss: 0.1310
- Precision: 0.8836
- Recall: 0.8933
- F1: 0.8884
- Accuracy: 0.9760
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: 8
- eval_batch_size: 8
- 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 |
|---|---|---|---|---|---|---|---|
| 0.043 | 1.0 | 32820 | 0.0755 | 0.8778 | 0.8773 | 0.8775 | 0.9745 |
| 0.0181 | 2.0 | 65640 | 0.1010 | 0.8913 | 0.8754 | 0.8833 | 0.9756 |
| 0.0075 | 3.0 | 98460 | 0.1310 | 0.8836 | 0.8933 | 0.8884 | 0.9760 |
Framework versions
- Transformers 4.36.0.dev0
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
- Task
- Token Classification
- Library
- transformers
- Type
- distilbert
- License
- apache-2.0
- Created
- Dec 5, 2023
- Updated
- Dec 6, 2023