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albert-base-v2-cased-ner
TunahanGokcimen/albert-base-v2-cased-ner
albert-base-v2-cased-ner is a token classification model from TunahanGokcimen. 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
- albert
- Access
- Public
- Created
- Jan 26, 2024
- Updated
- Jan 26, 2024
- SHA
- d8bd802d
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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. -->
albert-base-v2-cased-ner
This model is a fine-tuned version of albert/albert-base-v2 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.1912
- Precision: 0.7496
- Recall: 0.8064
- F1: 0.7770
- Accuracy: 0.9384
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 |
|---|---|---|---|---|---|---|---|
| 0.225 | 1.0 | 2078 | 0.2281 | 0.6778 | 0.7389 | 0.7070 | 0.9224 |
| 0.1849 | 2.0 | 4156 | 0.1909 | 0.7194 | 0.8032 | 0.7590 | 0.9360 |
| 0.1379 | 3.0 | 6234 | 0.1912 | 0.7496 | 0.8064 | 0.7770 | 0.9384 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
- Task
- Token Classification
- Library
- transformers
- Type
- albert
- License
- apache-2.0
- Created
- Jan 26, 2024
- Updated
- Jan 26, 2024