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glot500_fintuned_en_ewt
ibrahimbukhari1998/glot500_fintuned_en_ewt
glot500_fintuned_en_ewt is a token classification model from ibrahimbukhari1998. 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
- xlm-roberta
- Access
- Public
- Created
- Nov 26, 2024
- Updated
- Nov 26, 2024
- SHA
- 075d5625
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Base models
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. -->
glot500_fintuned_en_ewt
This model is a fine-tuned version of cis-lmu/glot500-base on the universal_dependencies dataset. It achieves the following results on the evaluation set:
- Loss: 0.1532
- Precision: 0.9531
- Recall: 0.9549
- F1: 0.9540
- Accuracy: 0.9607
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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 2
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 1.0263 | 1.0 | 784 | 0.2097 | 0.9385 | 0.9394 | 0.9389 | 0.9477 |
| 0.1409 | 2.0 | 1568 | 0.1532 | 0.9531 | 0.9549 | 0.9540 | 0.9607 |
Framework versions
- Transformers 4.46.3
- Pytorch 2.5.1
- Datasets 3.1.0
- Tokenizers 0.20.3
- Task
- Token Classification
- Library
- transformers
- Type
- xlm-roberta
- License
- apache-2.0
- Created
- Nov 26, 2024
- Updated
- Nov 26, 2024