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--- |
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library_name: transformers |
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license: mit |
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base_model: xlm-roberta-base |
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tags: |
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- generated_from_trainer |
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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-fr-en-it |
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results: |
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- task: |
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type: token-classification |
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dataset: |
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name: google/xtreme |
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type: google/xtreme |
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metrics: |
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- name: f1 |
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type: f1 |
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value: 0.8380 |
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datasets: |
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- google/xtreme |
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language: |
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- de |
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- fr |
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- en |
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- it |
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pipeline_tag: token-classification |
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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-fr-en-it |
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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: 0.2148 |
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- F1: 0.8380 |
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## Model description |
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This model is fine-tuned for Named Entity Recognition (NER) in German, French, English, and Italian. |
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It identifies entities like persons, organizations, locations, etc... |
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## Intended uses & limitations |
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More information needed |
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## How to Use |
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```Python |
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from transformers import pipeline |
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# Load the NER pipeline |
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model_name = "avanishd/xlm-roberta-base-finetuned-panx-de-fr-en-it" |
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ner_pipeline = pipeline("token-classification", model=model_name, aggregation_strategy="simple") |
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# Example text (English, but you can use DE/FR/IT as well) |
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text = "Barack Obama was born in Hawaii and became President of the United States." |
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# Get NER predictions |
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entities = ner_pipeline(text) |
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# Display results |
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for entity in entities: |
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print(f"{entity['word']} → {entity['entity_group']} ({entity['score']:.2f})") |
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``` |
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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: 64 |
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- eval_batch_size: 64 |
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- seed: 42 |
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- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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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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| No log | 1.0 | 313 | 0.2470 | 0.7921 | |
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| No log | 2.0 | 626 | 0.2170 | 0.8318 | |
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| No log | 3.0 | 939 | 0.2148 | 0.8380 | |
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### Framework versions |
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- Transformers 4.50.3 |
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- Pytorch 2.6.0+cu124 |
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- Datasets 3.5.0 |
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- Tokenizers 0.21.1 |
