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bert-base-uncased-finetuned-ner
EffyLi/bert-base-uncased-finetuned-ner
bert-base-uncased-finetuned-ner is a token classification model from EffyLi. 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
- bert
- Access
- Public
- Created
- Dec 11, 2022
- Updated
- Dec 11, 2022
- SHA
- e931eb0c
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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. -->
bert-base-uncased-finetuned-ner
This model is a fine-tuned version of bert-base-uncased on the conll2003 dataset. It achieves the following results on the evaluation set:
- Loss: 0.0618
- Precision: 0.9145
- Recall: 0.9305
- F1: 0.9224
- Accuracy: 0.9826
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: 64
- eval_batch_size: 64
- 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 | 220 | 0.0809 | 0.8923 | 0.9051 | 0.8987 | 0.9784 |
| No log | 2.0 | 440 | 0.0643 | 0.9108 | 0.9262 | 0.9184 | 0.9817 |
| 0.1657 | 3.0 | 660 | 0.0618 | 0.9145 | 0.9305 | 0.9224 | 0.9826 |
Framework versions
- Transformers 4.18.0
- Pytorch 1.12.0
- Datasets 2.7.1
- Tokenizers 0.11.0
- Task
- Token Classification
- Library
- transformers
- Type
- bert
- License
- apache-2.0
- Created
- Dec 11, 2022
- Updated
- Dec 11, 2022