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:
- cbbb1e53e1bfe94670e3aff945a95e1516e9476aa5040da91faa789f86bd7602
- Size of remote file:
- 3.64 kB
- SHA256:
- 4ef555a9e3cf9eb1a49ea37d5d4da837b4c16f8737fcd33a0b6c4a08d3a9b94d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.