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langtok-bert
emmabedna/langtok-bert
langtok-bert 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
- Oct 7, 2025
- Updated
- Oct 7, 2025
- SHA
- faaee6dd
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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
This model is a fine-tuned version of google-bert/bert-base-multilingual-cased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0141
- Precision: 0.9355
- Recall: 0.9334
- F1: 0.9344
- Accuracy: 0.9961
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 |
|---|---|---|---|---|---|---|---|
| 0.1722 | 1.0 | 854 | 0.0163 | 0.9177 | 0.9035 | 0.9105 | 0.9949 |
| 0.0126 | 2.0 | 1708 | 0.0142 | 0.9298 | 0.9259 | 0.9279 | 0.9957 |
| 0.0071 | 3.0 | 2562 | 0.0141 | 0.9355 | 0.9334 | 0.9344 | 0.9961 |
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
- Oct 7, 2025
- Updated
- Oct 7, 2025