Instructions to use chintagunta85/electramed-small-ADE-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use chintagunta85/electramed-small-ADE-ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="chintagunta85/electramed-small-ADE-ner")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("chintagunta85/electramed-small-ADE-ner") model = AutoModelForTokenClassification.from_pretrained("chintagunta85/electramed-small-ADE-ner", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1953940880df67071698e76012bcee7fae95201c47f7ef492af7e42c8d494dec
- Size of remote file:
- 3.31 kB
- SHA256:
- b106e864105e932074e7eb9a415a798ad5f1ee71cfc21766a20b87e0b2df3d43
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.