Token Classification
Transformers
Safetensors
English
umt5
text2text-generation
ner
named-entity-recognition
biomedical
en-vimedner
Instructions to use nhuvo/umt5-base-en-vimedner-ner-en with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nhuvo/umt5-base-en-vimedner-ner-en with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="nhuvo/umt5-base-en-vimedner-ner-en")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("nhuvo/umt5-base-en-vimedner-ner-en") model = AutoModelForSeq2SeqLM.from_pretrained("nhuvo/umt5-base-en-vimedner-ner-en", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0d6117bacd88e92c9978981e3a70c5fbb2d27b17c24cfbb491c0b13e503cd811
- Size of remote file:
- 16.8 MB
- SHA256:
- 2118f98dae4427dba302ed8e421676859166a4bf8afc814857507091a1d44efe
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