Feature Extraction
Transformers
Safetensors
English
bert
token-classification
text-embeddings-inference
Instructions to use noystl/scibert_token_classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use noystl/scibert_token_classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="noystl/scibert_token_classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("noystl/scibert_token_classifier") model = AutoModelForTokenClassification.from_pretrained("noystl/scibert_token_classifier") - Notebooks
- Google Colab
- Kaggle
Add pipeline tag (#1)
Browse files- Add pipeline tag (9c2d1aa74b5120ff32049e974ebc16aebc3d8e47)
Co-authored-by: Niels Rogge <nielsr@users.noreply.huggingface.co>
README.md
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license: cc
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language:
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- en
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base_model:
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- allenai/scibert_scivocab_uncased
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datasets:
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- noystl/Recombination-Extraction
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library_name: transformers
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This Hugging Face repository contains a fine-tuned [allenai/scibert_scivocab_uncased](https://huggingface.co/allenai/scibert_scivocab_uncased) model trained for the task of extracting recombination examples from scientific abstracts, as described in the paper [CHIMERA: A Knowledge Base of Idea Recombination in Scientific Literature](https://huggingface.co/papers/2505.20779).
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base_model:
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- allenai/scibert_scivocab_uncased
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datasets:
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- noystl/Recombination-Extraction
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language:
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- en
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library_name: transformers
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license: cc
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pipeline_tag: feature-extraction
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---
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This Hugging Face repository contains a fine-tuned [allenai/scibert_scivocab_uncased](https://huggingface.co/allenai/scibert_scivocab_uncased) model trained for the task of extracting recombination examples from scientific abstracts, as described in the paper [CHIMERA: A Knowledge Base of Idea Recombination in Scientific Literature](https://huggingface.co/papers/2505.20779).
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