Instructions to use ktgiahieu/RoBERTa-large-PM-M3-Voc-hf-finetuned-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ktgiahieu/RoBERTa-large-PM-M3-Voc-hf-finetuned-ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="ktgiahieu/RoBERTa-large-PM-M3-Voc-hf-finetuned-ner")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("ktgiahieu/RoBERTa-large-PM-M3-Voc-hf-finetuned-ner") model = AutoModelForTokenClassification.from_pretrained("ktgiahieu/RoBERTa-large-PM-M3-Voc-hf-finetuned-ner", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- fa57670cf2222528451ff6365a145c7d4abbf31990b075c0e08a4e84d976de9d
- Size of remote file:
- 1.42 GB
- SHA256:
- 14e2692778359267a0b596c083cff91f340059b734a200fa44f6670939f02983
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