Update README.md

README.md

@@ -87,6 +87,104 @@ model = AutoModelForTokenClassification.from_pretrained("xlm-roberta-large-finet
 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,