Instructions to use TweebankNLP/bertweet-tb2-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TweebankNLP/bertweet-tb2-ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="TweebankNLP/bertweet-tb2-ner")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("TweebankNLP/bertweet-tb2-ner") model = AutoModelForTokenClassification.from_pretrained("TweebankNLP/bertweet-tb2-ner", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f879bcf42c39a989c98e6cac5cd298958232b3710fc6eec9fe12539ed2f9c40c
- Size of remote file:
- 537 MB
- SHA256:
- 14a62f19ad404ac8ebbbd8ab38dc61349457b0a433f2dcd59008c300bce675a8
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.