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langtok-bert_uncased
emmabedna/langtok-bert_uncased
langtok-bert_uncased is a token classification model from emmabedna. 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
- Nov 11, 2025
- Updated
- Nov 11, 2025
- SHA
- 99bae58a
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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. -->
langtok-bert_uncased
This model is a fine-tuned version of google-bert/bert-base-multilingual-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0170
- Precision: 0.9227
- Recall: 0.9267
- F1: 0.9247
- Accuracy: 0.9952
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: 32
- eval_batch_size: 32
- 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 |
|---|---|---|---|---|---|---|---|
| 0.1943 | 1.0 | 854 | 0.0200 | 0.9041 | 0.8954 | 0.8997 | 0.9939 |
| 0.0162 | 2.0 | 1708 | 0.0168 | 0.9185 | 0.9210 | 0.9197 | 0.9950 |
| 0.0085 | 3.0 | 2562 | 0.0170 | 0.9227 | 0.9267 | 0.9247 | 0.9952 |
Framework versions
- Transformers 4.44.2
- Pytorch 2.7.1+cu126
- Datasets 3.0.1
- Tokenizers 0.19.1
- Task
- Token Classification
- Library
- transformers
- Type
- bert
- License
- apache-2.0
- Created
- Nov 11, 2025
- Updated
- Nov 11, 2025