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 pytorch_model.bin from pranav-s/PolymerNER: direct link, hf CLI and curl.
- Browser
- Download file 436 MB
-
https://huggingface.co/pranav-s/PolymerNER/resolve/refs%2Fpr%2F2/pytorch_model.bin
- Command line
-
hf download hf://pranav-s/PolymerNER@refs/pr/2/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/pranav-s/PolymerNER/resolve/refs%2Fpr%2F2/pytorch_model.bin
436 MB
- Xet hash:
- 91b0618bf70f27036aa224e5c9dc97f21aa70b84065c9220a367afc46dde60bd
- Size of remote file:
- 436 MB
- SHA256:
- 73c7ad087af05c9dbd3b8b99d4550a7b8e5eccf6c10efa7e445c505f579478ad
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