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'}
  ]from datasets import load_dataset

dataset = load_dataset("debatelab/deepa2")
{"license": "apache-2.0"}
doi:10.57967/hf/0265 license:apache-2.0 region:us
TAGS
#doi-10.57967/hf/0265 #license-apache-2.0 #region-us 

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'}
  ]from datasets import load_dataset

dataset = load_dataset("debatelab/deepa2")
TAGS
#doi-10.57967/hf/0265 #license-apache-2.0 #region-us 

TAGS
#doi-10.57967/hf/0265 #license-apache-2.0 #region-us 
