Instructions to use ktgiahieu/RoBERTa-base-PM-M3-Voc-distill-align-hf-finetuned-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ktgiahieu/RoBERTa-base-PM-M3-Voc-distill-align-hf-finetuned-ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="ktgiahieu/RoBERTa-base-PM-M3-Voc-distill-align-hf-finetuned-ner")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("ktgiahieu/RoBERTa-base-PM-M3-Voc-distill-align-hf-finetuned-ner") model = AutoModelForTokenClassification.from_pretrained("ktgiahieu/RoBERTa-base-PM-M3-Voc-distill-align-hf-finetuned-ner", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a10cb1c14f0554dc2c966ef4830d5555fa4d812035cfd35d47c7ee6bc5398341
- Size of remote file:
- 496 MB
- SHA256:
- b17ec012d8436dfc3bb147453e90e307aee75355e88f27e5a614ca252cc7158e
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