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xlm-roberta-base-finetuned-panx-all
transformersbook/xlm-roberta-base-finetuned-panx-all
xlm-roberta-base-finetuned-panx-all is a token classification model from transformersbook. 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
- Mar 2, 2022
- Updated
- Jun 25, 2022
- SHA
- 39aaafd0
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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. -->
xlm-roberta-base-finetuned-panx-all
This model is a fine-tuned version of xlm-roberta-base on the PAN-X dataset. The model is trained in Chapter 4: Multilingual Named Entity Recognition in the NLP with Transformers book. You can find the full code in the accompanying Github repository.
It achieves the following results on the evaluation set:
- Loss: 0.1739
- F1: 0.8581
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: 24
- eval_batch_size: 24
- 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 | F1 |
|---|---|---|---|---|
| 0.2912 | 1.0 | 835 | 0.1883 | 0.8238 |
| 0.1548 | 2.0 | 1670 | 0.1738 | 0.8480 |
| 0.101 | 3.0 | 2505 | 0.1739 | 0.8581 |
Framework versions
- Transformers 4.12.0.dev0
- Pytorch 1.9.1+cu102
- Datasets 1.12.1
- Tokenizers 0.10.3
- Task
- Token Classification
- Library
- transformers
- Type
- xlm-roberta
- License
- mit
- Created
- Mar 2, 2022
- Updated
- Jun 25, 2022