license: wtfpl
from transformers import pipeline tokenizer = AutoTokenizer.from_pretrained("xlm-roberta-large-finetuned-conll03-english") model = AutoModelForTokenClassification.from_pretrained("xlm-roberta-large-finetuned-conll03-english") classifier = pipeline("ner", model=model, tokenizer=tokenizer) classifier("Alya told Jasmine that Andrew could pay with cash..") [{'end': 2, 'entity': 'I-PER', 'index': 1, 'score': 0.9997861, 'start': 0, 'word': '▁Al'}, {'end': 4, 'entity': 'I-PER', 'index': 2, 'score': 0.9998591, 'start': 2, 'word': 'ya'}, {'end': 16, 'entity': 'I-PER', 'index': 4, 'score': 0.99995816, 'start': 10, 'word': '▁Jasmin'}, {'end': 17, 'entity': 'I-PER', 'index': 5, 'score': 0.9999584, 'start': 16, 'word': 'e'}, {'end': 29, 'entity': 'I-PER', 'index': 7, 'score': 0.99998057, 'start': 23, 'word': '▁Andrew'}]
Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model.
Training See the following resources for training data and training procedure details:
XLM-RoBERTa-large model card CoNLL-2003 data card Associated paper Evaluation See the associated paper for evaluation details.
Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
Hardware Type: 500 32GB Nvidia V100 GPUs (from the associated paper) Hours used: More information needed Cloud Provider: More information needed Compute Region: More information needed Carbon Emitted: More information needed Technical Specifications See the associated paper for further details.
Citation BibTeX:
@article{conneau2019unsupervised, title={Unsupervised Cross-lingual Representation Learning at Scale}, author={Conneau, Alexis and Khandelwal, Kartikay and Goyal, Naman and Chaudhary, Vishrav and Wenzek, Guillaume and Guzm{'a}n, Francisco and Grave, Edouard and Ott, Myle and Zettlemoyer, Luke and Stoyanov, Veselin}, journal={arXiv preprint arXiv:1911.02116}, year={2019} }
APA:
Conneau, A., Khandelwal, K., Goyal, N., Chaudhary, V., Wenzek, G., Guzmán, F., ... & Stoyanov, V. (2019). Unsupervised cross-lingual representation learning at scale. arXiv preprint arXiv:1911.02116. Model Card Authors This model card was written by the team at Hugging Face.
How to Get Started with the Model Use the code below to get started with the model. You can use this model directly within a pipeline for NER.
Click to expand from transformers import AutoTokenizer, AutoModelForTokenClassification from transformers import pipeline tokenizer = AutoTokenizer.from_pretrained("xlm-roberta-large-finetuned-conll03-english") model = AutoModelForTokenClassification.from_pretrained("xlm-roberta-large-finetuned-conll03-english") classifier = pipeline("ner", model=model, tokenizer=tokenizer) classifier("Hello I'm Omar and I live in Zürich.")
[{'end': 14, 'entity': 'I-PER', 'index': 5, 'score': 0.9999175, 'start': 10, 'word': '▁Omar'}, {'end': 35, 'entity': 'I-LOC', 'index': 10, 'score': 0.9999906, 'start': 29, 'word': '▁Zürich'}] from transformers import pipeline tokenizer = AutoTokenizer.from_pretrained("xlm-roberta-large-finetuned-conll03-english") model = AutoModelForTokenClassification.from_pretrained("xlm-roberta-large-finetuned-conll03-english") classifier = pipeline("ner", model=model, tokenizer=tokenizer) classifier("Alya told Jasmine that Andrew could pay with cash..") [{'end': 2, 'entity': 'I-PER', 'index': 1, 'score': 0.9997861, 'start': 0, 'word': '▁Al'}, {'end': 4, 'entity': 'I-PER', 'index': 2, 'score': 0.9998591, 'start': 2, 'word': 'ya'}, {'end': 16, 'entity': 'I-PER', 'index': 4, 'score': 0.99995816, 'start': 10, 'word': '▁Jasmin'}, {'end': 17, 'entity': 'I-PER', 'index': 5, 'score': 0.9999584, 'start': 16, 'word': 'e'}, {'end': 29, 'entity': 'I-PER', 'index': 7, 'score': 0.99998057, 'start': 23, 'word': '▁Andrew'}]
Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model.
Training See the following resources for training data and training procedure details:
XLM-RoBERTa-large model card CoNLL-2003 data card Associated paper Evaluation See the associated paper for evaluation details.
Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
Hardware Type: 500 32GB Nvidia V100 GPUs (from the associated paper) Hours used: More information needed Cloud Provider: More information needed Compute Region: More information needed Carbon Emitted: More information needed Technical Specifications See the associated paper for further details.
Citation BibTeX:
@article{conneau2019unsupervised, title={Unsupervised Cross-lingual Representation Learning at Scale}, author={Conneau, Alexis and Khandelwal, Kartikay and Goyal, Naman and Chaudhary, Vishrav and Wenzek, Guillaume and Guzm{'a}n, Francisco and Grave, Edouard and Ott, Myle and Zettlemoyer, Luke and Stoyanov, Veselin}, journal={arXiv preprint arXiv:1911.02116}, year={2019} }
APA:
Conneau, A., Khandelwal, K., Goyal, N., Chaudhary, V., Wenzek, G., Guzmán, F., ... & Stoyanov, V. (2019). Unsupervised cross-lingual representation learning at scale. arXiv preprint arXiv:1911.02116. Model Card Authors This model card was written by the team at Hugging Face.
How to Get Started with the Model Use the code below to get started with the model. You can use this model directly within a pipeline for NER.
Click to expand from transformers import AutoTokenizer, AutoModelForTokenClassification from transformers import pipeline tokenizer = AutoTokenizer.from_pretrained("xlm-roberta-large-finetuned-conll03-english") model = AutoModelForTokenClassification.from_pretrained("xlm-roberta-large-finetuned-conll03-english") classifier = pipeline("ner", model=model, tokenizer=tokenizer) classifier("Hello I'm Omar and I live in Zürich.")
[{'end': 14, 'entity': 'I-PER', 'index': 5, 'score': 0.9999175, 'start': 10, 'word': '▁Omar'}, {'end': 35, 'entity': 'I-LOC', 'index': 10, 'score': 0.9999906, 'start': 29, 'word': '▁Zürich'}]