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xlm-roberta-base-finetuned-product-ner
JamesLemo/xlm-roberta-base-finetuned-product-ner
xlm-roberta-base-finetuned-product-ner is a token classification model from JamesLemo. 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
- Nov 29, 2025
- Updated
- Nov 30, 2025
- SHA
- 0dd179a4
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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. -->
xlm-roberta-base-finetuned-product-ner
This model is a fine-tuned version of xlm-roberta-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0076
- F1: 0.9941
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: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|---|---|---|---|---|
| 0.1104 | 1.0 | 702 | 0.0318 | 0.9813 |
| 0.0226 | 2.0 | 1404 | 0.0204 | 0.9822 |
| 0.0096 | 3.0 | 2106 | 0.0107 | 0.9907 |
| 0.0048 | 4.0 | 2808 | 0.0080 | 0.9926 |
| 0.0023 | 5.0 | 3510 | 0.0076 | 0.9941 |
Framework versions
- Transformers 4.57.2
- Pytorch 2.9.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.1
- Task
- Token Classification
- Library
- transformers
- Type
- xlm-roberta
- License
- mit
- Created
- Nov 29, 2025
- Updated
- Nov 30, 2025