| Hugging Face's logo | |
| --- | |
| language: | |
| - ar | |
| - as | |
| - bn | |
| - ca | |
| - en | |
| - es | |
| - eu | |
| - fr | |
| - gu | |
| - hi | |
| - id | |
| - ig | |
| - mr | |
| - pa | |
| - pt | |
| - sw | |
| - ur | |
| - vi | |
| - yo | |
| - zh | |
| - multilingual | |
| datasets: | |
| - wikiann | |
| --- | |
| # xlm-roberta-base-wikiann-ner | |
| ## Model description | |
| **xlm-roberta-base-wikiann-ner** is the first **Named Entity Recognition** model for 20 languages (Arabic, Assamese, Bengali, Catalan, English, Spanish, Basque, French, Gujarati, Hindi, Indonesia, Igbo, Marathi, Punjabi, Portugues and Swahili, Urdu, Vietnamese, Yoruba, Chinese) based on a fine-tuned XLM-RoBERTa large model. It achieves the **state-of-the-art performance** for the NER task. It has been trained to recognize three types of entities: location (LOC), organizations (ORG), and person (PER). | |
| Specifically, this model is a *xlm-roberta-large* model that was fine-tuned on an aggregation of languages datasets obtained from [WikiANN](https://huggingface.co/datasets/wikiann) dataset. | |
| ## Intended uses & limitations | |
| #### How to use | |
| You can use this model with Transformers *pipeline* for NER. | |
| ```python | |
| from transformers import AutoTokenizer, AutoModelForTokenClassification | |
| from transformers import pipeline | |
| tokenizer = AutoTokenizer.from_pretrained("xlm-roberta-base-wikiann-ner") | |
| model = AutoModelForTokenClassification.from_pretrained("xlm-roberta-base-wikiann-ner") | |
| nlp = pipeline("ner", model=model, tokenizer=tokenizer) | |
| example = "Ìbọn ń ró kù kù gẹ́gẹ́ bí ọwọ́ ọ̀pọ̀ aráàlù ṣe tẹ ìbọn ní Kyiv láti dojú kọ Russia" | |
| ner_results = nlp(example) | |
| print(ner_results) | |
| ``` | |
| #### 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 | |
| This model was fine-tuned on 20 NER datasets (Arabic, Assamese, Bengali, Catalan, English, Spanish, Basque, French, Gujarati, Hindi, Indonesia, Igbo, Marathi, Punjabi, Portugues and Swahili, Urdu, Vietnamese, Yoruba, Chinese)[wikiann](https://huggingface.co/datasets/wikiann). | |
| The training dataset distinguishes between the beginning and continuation of an entity so that if there are back-to-back entities of the same type, the model can output where the second entity begins. As in the dataset, each token will be classified as one of the following classes: | |
| Abbreviation|Description | |
| -|- | |
| O|Outside of a named entity | |
| B-PER |Beginning of a person’s name right after another person’s name | |
| I-PER |Person’s name | |
| B-ORG |Beginning of an organisation right after another organisation | |
| I-ORG |Organisation | |
| B-LOC |Beginning of a location right after another location | |
| I-LOC |Location | |
| ### BibTeX entry and citation info | |
| ``` | |