Instructions to use semaj83/scibert_finetuned_ctmatch with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use semaj83/scibert_finetuned_ctmatch with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="semaj83/scibert_finetuned_ctmatch")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("semaj83/scibert_finetuned_ctmatch") model = AutoModelForSequenceClassification.from_pretrained("semaj83/scibert_finetuned_ctmatch", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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README.md
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Fine-tuned from 'allenai/scibert_scivocab_uncased' on triples of labelled topic, documents, relevance labels.
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These triples were processed using ctproc, collated from the openly available TREC22 Precision Medicine and CSIRO datasets here:
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https://huggingface.co/datasets/semaj83/
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### Model Description
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Fine-tuned from 'allenai/scibert_scivocab_uncased' on triples of labelled topic, documents, relevance labels.
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These triples were processed using ctproc, collated from the openly available TREC22 Precision Medicine and CSIRO datasets here:
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https://huggingface.co/datasets/semaj83/ctmatch_classification
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### Model Description
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