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")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("pranav-s/PolymerNER") model = AutoModelForTokenClassification.from_pretrained("pranav-s/PolymerNER") - Notebooks
- Google Colab
- Kaggle
Adding `safetensors` variant of this model
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by SFconvertbot - opened
- model.safetensors +3 -0
model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:d8980c7448d101b3411c7ff71b955011f7c3b86836910d17d661e62d3e20dda0
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size 435621804
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