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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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model-index: |
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- name: xlm-roberta-base-finetuned-panx-de |
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results: |
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- task: |
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name: Token Classification |
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type: token-classification |
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dataset: |
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name: xtreme |
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type: xtreme |
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config: PAN-X.de |
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split: validation |
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args: PAN-X.de |
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metrics: |
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- name: F1 |
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type: f1 |
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value: 0.8637881274404392 |
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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-de |
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This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1569 |
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- F1: 0.8638 |
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## Model description |
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Multilingual Named Entity Recognition across several languages |
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For this project's token classification, I built a unique custom model head and the |
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WikiANN or PAN-X.2, which is a subset of the Cross-lingual TRansfer Evaluation of Multilingual |
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Encoders (XTREME) benchmark, was applied. This project was completed for a customer based |
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in switzerland, where the four languages that are most frequently spoken are |
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German (62.9% of articles), French (22.9%), Italian (8.4%), and English (5.9%). |
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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: 5 |
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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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| 0.3044 | 1.0 | 525 | 0.1598 | 0.8174 | |
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| 0.1462 | 2.0 | 1050 | 0.1527 | 0.8308 | |
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| 0.1006 | 3.0 | 1575 | 0.1487 | 0.8459 | |
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| 0.0698 | 4.0 | 2100 | 0.1431 | 0.8615 | |
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| 0.0472 | 5.0 | 2625 | 0.1569 | 0.8638 | |
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### Framework versions |
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- Transformers 4.26.1 |
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- Pytorch 1.13.1+cu116 |
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- Datasets 2.10.0 |
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- Tokenizers 0.13.2 |
