---
language: 
- de
- en
- multilingual
widget:
- 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."
- 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."
- text: "My name is Julian and I live in montreal"
- text: "My name is clara and I live in berkeley, california."
- text: "My name is wolfgang and I live in berlin"
tags:
- roberta
license: mit
datasets:
- wikiann
---

# Roberta for Multilingual Named Entity Recognition

## Model description

#### Limitations and bias
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.  

## Training data


## Usage

```python

model_tuned = RobertaForTokenClassification.from_pretrained("./results/checkpoint-final/")

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."

inputs = tokenizer(
    text, 
    add_special_tokens=False, return_tensors="pt"
)

with torch.no_grad():
    logits = model_tuned(**inputs).logits

predicted_token_class_ids = logits.argmax(-1)

# Note that tokens are classified rather then input words which means that
# there might be more predicted token classes than words.
# Multiple token classes might account for the same word
predicted_tokens_classes = [model_tuned.config.id2label[t.item()] for t in predicted_token_class_ids[0]]
predicted_tokens_classes
```