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--- |
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language: |
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- de |
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- en |
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- multilingual |
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widget: |
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- text: "In December 1903 in France the Royal Swedish Academy of Sciences awarded Pierre Curie, Marie Curie, and Henri Becquerel the Nobel Prize in Physics." |
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- text: "Für Richard Phillips Feynman war es immer wichtig in New York, die unanschaulichen Gesetzmäßigkeiten der Quantenphysik Laien und Studenten nahezubringen und verständlich zu machen." |
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- text: "My name is Julian and I live in montreal" |
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- text: "My name is clara and I live in berkeley, california." |
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- text: "My name is wolfgang and I live in berlin" |
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tags: |
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- roberta |
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license: mit |
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datasets: |
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- wikiann |
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--- |
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# Roberta for Multilingual Named Entity Recognition |
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## Model description |
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#### Limitations and bias |
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This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains. |
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## Training data |
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## Usage |
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```python |
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model_tuned = RobertaForTokenClassification.from_pretrained("./results/checkpoint-final/") |
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text = "Für Richard Phillips Feynman war es immer wichtig in New York, die unanschaulichen Gesetzmäßigkeiten der Quantenphysik Laien und Studenten nahezubringen und verständlich zu machen." |
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inputs = tokenizer( |
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text, |
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add_special_tokens=False, return_tensors="pt" |
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) |
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with torch.no_grad(): |
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logits = model_tuned(**inputs).logits |
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predicted_token_class_ids = logits.argmax(-1) |
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# Note that tokens are classified rather then input words which means that |
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# there might be more predicted token classes than words. |
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# Multiple token classes might account for the same word |
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predicted_tokens_classes = [model_tuned.config.id2label[t.item()] for t in predicted_token_class_ids[0]] |
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predicted_tokens_classes |
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``` |
