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--- |
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license: mit |
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tags: |
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- generated_from_trainer |
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datasets: |
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- xtreme |
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metrics: |
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- f1 |
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base_model: xlm-roberta-base |
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model-index: |
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- name: xlm-roberta-base-finetuned-panx-it |
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results: |
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- task: |
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type: token-classification |
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name: Token Classification |
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dataset: |
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name: xtreme |
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type: xtreme |
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args: PAN-X.it |
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metrics: |
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- type: f1 |
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value: 0.8215158924205379 |
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name: F1 |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# xlm-roberta-base-finetuned-panx-it |
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This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/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](https://learning.oreilly.com/library/view/natural-language-processing/9781098103231/). You can find the full code in the accompanying [Github repository](https://github.com/nlp-with-transformers/notebooks/blob/main/04_multilingual-ner.ipynb). |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2445 |
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- F1: 0.8215 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 24 |
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- eval_batch_size: 24 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 3 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:------:| |
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| 0.7594 | 1.0 | 70 | 0.3402 | 0.7467 | |
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| 0.2942 | 2.0 | 140 | 0.2555 | 0.7971 | |
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| 0.1814 | 3.0 | 210 | 0.2445 | 0.8215 | |
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### Framework versions |
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- Transformers 4.12.0.dev0 |
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- Pytorch 1.9.1+cu102 |
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- Datasets 1.12.1 |
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- Tokenizers 0.10.3 |
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