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mobilebert-uncased-finetuned-ner
koosty/mobilebert-uncased-finetuned-ner
mobilebert-uncased-finetuned-ner is a token classification model from koosty. 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
- mobilebert
- Access
- Public
- Created
- May 18, 2023
- Updated
- May 18, 2023
- SHA
- c12e3d01
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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. -->
mobilebert-uncased-finetuned-ner
This model is a fine-tuned version of google/mobilebert-uncased on the id_nergrit_corpus dataset. It achieves the following results on the evaluation set:
- Loss: 0.3800
- Precision: 0.6700
- Recall: 0.6136
- F1: 0.6406
- Accuracy: 0.8974
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: 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 |
|---|---|---|---|---|---|---|---|
| 0.6239 | 1.0 | 1567 | 0.4989 | 0.5842 | 0.4877 | 0.5316 | 0.8688 |
| 0.5356 | 2.0 | 3134 | 0.4003 | 0.6368 | 0.5879 | 0.6113 | 0.8905 |
| 0.4035 | 3.0 | 4701 | 0.3800 | 0.6700 | 0.6136 | 0.6406 | 0.8974 |
Framework versions
- Transformers 4.29.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
- Task
- Token Classification
- Library
- transformers
- Type
- mobilebert
- License
- apache-2.0
- Created
- May 18, 2023
- Updated
- May 18, 2023