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XLMR-Cross-Lingual-NER
Sharpaxis/XLMR-Cross-Lingual-NER
XLMR-Cross-Lingual-NER is a token classification model from Sharpaxis. Use it when you need labels on individual words, such as names. It is set up for transformers. The card lists the license as mit.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Token Classification
- Library
- transformers
- License
- mit
- Model type
- xlm-roberta
- Access
- Public
- Created
- Dec 15, 2024
- Updated
- Dec 15, 2024
- SHA
- e607c901
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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. -->
XLMR-Cross-Lingual-NER
This model is a fine-tuned version of xlm-roberta-base on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.1420
- F1: 0.8621
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 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: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|---|---|---|---|---|
| 0.2896 | 1.0 | 787 | 0.1600 | 0.8287 |
| 0.1374 | 2.0 | 1574 | 0.1421 | 0.8560 |
| 0.1017 | 3.0 | 2361 | 0.1420 | 0.8621 |
Framework versions
- Transformers 4.46.3
- Pytorch 2.4.0
- Datasets 3.1.0
- Tokenizers 0.20.3
- Task
- Token Classification
- Library
- transformers
- Type
- xlm-roberta
- License
- mit
- Created
- Dec 15, 2024
- Updated
- Dec 15, 2024