Feature Extraction
Transformers
PyTorch
English
bert
token-classification
materials
text-embeddings-inference
Instructions to use pranav-s/PolymerNER with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pranav-s/PolymerNER with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="pranav-s/PolymerNER")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("pranav-s/PolymerNER") model = AutoModelForTokenClassification.from_pretrained("pranav-s/PolymerNER", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from pranav-s/PolymerNER: direct link, hf CLI and curl.
- Browser
- Download file 706 kB
-
https://huggingface.co/pranav-s/PolymerNER/resolve/refs%2Fpr%2F2/tokenizer.json
- Command line
-
hf download hf://pranav-s/PolymerNER@refs/pr/2/tokenizer.json
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curl -L -o tokenizer.json https://huggingface.co/pranav-s/PolymerNER/resolve/refs%2Fpr%2F2/tokenizer.json
706 kB
File too large to display, you can check the raw version instead.