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anercorpDataset_v2.1
terzimert/anercorpDataset_v2.1
anercorpDataset_v2.1 is a token classification model from terzimert. 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
- Apr 24, 2023
- Updated
- Apr 24, 2023
- SHA
- 84385c68
Try a prompt
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. -->
anercorpDataset_v2.1
This model is a fine-tuned version of CAMeL-Lab/bert-base-arabic-camelbert-mix-ner on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.2755
- Precision: 0.7604
- Recall: 0.6878
- F1: 0.7223
- Accuracy: 0.9461
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 |
|---|---|---|---|---|---|---|---|
| 0.206 | 1.0 | 7057 | 0.2703 | 0.6971 | 0.6367 | 0.6655 | 0.9377 |
| 0.2036 | 2.0 | 14114 | 0.2438 | 0.7460 | 0.6756 | 0.7090 | 0.9449 |
| 0.1404 | 3.0 | 21171 | 0.2755 | 0.7604 | 0.6878 | 0.7223 | 0.9461 |
Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
- Task
- Token Classification
- Library
- transformers
- Type
- bert
- License
- apache-2.0
- Created
- Apr 24, 2023
- Updated
- Apr 24, 2023