Instructions to use QCRI/bert-base-multilingual-cased-pos-english with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use QCRI/bert-base-multilingual-cased-pos-english with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="QCRI/bert-base-multilingual-cased-pos-english")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("QCRI/bert-base-multilingual-cased-pos-english") model = AutoModelForTokenClassification.from_pretrained("QCRI/bert-base-multilingual-cased-pos-english", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update usage to work with latest `transformers`
Browse files
README.md
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@@ -21,7 +21,7 @@ model_name = "QCRI/bert-base-multilingual-cased-pos-english"
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model = AutoModelForTokenClassification.from_pretrained(model_name)
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pipeline = TokenClassificationPipeline(model, tokenizer)
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outputs = pipeline("A test example")
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print(outputs)
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```
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model = AutoModelForTokenClassification.from_pretrained(model_name)
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pipeline = TokenClassificationPipeline(model=model, tokenizer=tokenizer)
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outputs = pipeline("A test example")
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print(outputs)
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```
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