Instructions to use GraphBot/model-pt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use GraphBot/model-pt with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="GraphBot/model-pt")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("GraphBot/model-pt") model = AutoModelForTokenClassification.from_pretrained("GraphBot/model-pt", device_map="auto") - Notebooks
- Google Colab
- Kaggle
|
Download README.md from GraphBot/model-pt: direct link, hf CLI and curl.
- Browser
- Download file 364 Bytes
-
https://huggingface.co/GraphBot/model-pt/resolve/main/README.md
- Command line
-
hf download hf://GraphBot/model-pt/README.md
-
curl -L -o README.md https://huggingface.co/GraphBot/model-pt/resolve/main/README.md
364 Bytes
metadata
library_name: transformers
tags: []
Model Card for Model ID
This is a Natural Entity Recognition Model built to recognise variable names and graph types for use in a bioinformatics graphing tool. It was build by Sarina Chai, Daichi Ito and Harry Martens as part of a UNSW DESN2000 Project
The model is built upon the existing BERT-BASED-UNCASED model.