Instructions to use gaussalgo/xlm-roberta-large_extractive-QA_en-cs with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gaussalgo/xlm-roberta-large_extractive-QA_en-cs with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="gaussalgo/xlm-roberta-large_extractive-QA_en-cs")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("gaussalgo/xlm-roberta-large_extractive-QA_en-cs") model = AutoModelForQuestionAnswering.from_pretrained("gaussalgo/xlm-roberta-large_extractive-QA_en-cs") - Notebooks
- Google Colab
- Kaggle
Commit ·
e09c0df
1
Parent(s): 9b09145
Update README.md
Browse files
README.md
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@@ -40,7 +40,7 @@ inputs = tokenizer(question, context, return_tensors="pt")
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outputs = model(**inputs)
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start_position = outputs.start_logits[0].argmax()
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end_position = outputs.end_logits[0].argmax()
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answer_ids =
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print("Answer:")
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print(tokenizer.decode(answer_ids))
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outputs = model(**inputs)
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start_position = outputs.start_logits[0].argmax()
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end_position = outputs.end_logits[0].argmax()
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answer_ids = inputs["input_ids"][0][start_position:end_position]
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print("Answer:")
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print(tokenizer.decode(answer_ids))
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