Text Classification
Transformers
PyTorch
English
bert
pubmed
arxiv
representations
scientific documents
text-embeddings-inference
Instructions to use arazd/MIReAD with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use arazd/MIReAD with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="arazd/MIReAD")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("arazd/MIReAD") model = AutoModelForSequenceClassification.from_pretrained("arazd/MIReAD") - Notebooks
- Google Colab
- Kaggle
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- text: "Tissue-based diagnostics and research is incessantly evolving with the development of new molecular tools. It has long been realized that immunohistochemistry can add an important new level of information on top of morphology and that protein expression patterns in a cancer may yield crucial diagnostic and prognostic information. We have generated an immunohistochemistry-based map of protein expression profiles in normal tissues, cancer and cell lines."
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This is the finetuned model presented in **MIReAD: a simple method for learning high-quality representations from
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scientific documents (ACL 2023)**.
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- bert
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widget:
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- text: "Tissue-based diagnostics and research is incessantly evolving with the development of new molecular tools. It has long been realized that immunohistochemistry can add an important new level of information on top of morphology and that protein expression patterns in a cancer may yield crucial diagnostic and prognostic information. We have generated an immunohistochemistry-based map of protein expression profiles in normal tissues, cancer and cell lines."
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- example_title: "Predicted journal:"
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This is the finetuned model presented in **MIReAD: a simple method for learning high-quality representations from
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scientific documents (ACL 2023)**.
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