Instructions to use ktgiahieu/RoBERTa-large-PM-M3-Voc-hf-finetuned-ner-v2 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-v2 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-v2")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("ktgiahieu/RoBERTa-large-PM-M3-Voc-hf-finetuned-ner-v2") model = AutoModelForTokenClassification.from_pretrained("ktgiahieu/RoBERTa-large-PM-M3-Voc-hf-finetuned-ner-v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c9aeab0593bf7f43b3793437dd5c209bf8df48a60f579c7cf26270ed7d270a40
- Size of remote file:
- 1.42 GB
- SHA256:
- 7631ccec04f921261f5d195dfb5548b1e43752905b099033cb5b77b16bf3bc6a
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