| --- |
| 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'}] |
| |
