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