Text Classification
Transformers
PyTorch
TensorBoard
bert
metascience
psychology
openscience
abstracts
text-embeddings-inference
Instructions to use ClinicalMetaScience/NegativeResultDetector with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ClinicalMetaScience/NegativeResultDetector with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ClinicalMetaScience/NegativeResultDetector")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ClinicalMetaScience/NegativeResultDetector") model = AutoModelForSequenceClassification.from_pretrained("ClinicalMetaScience/NegativeResultDetector", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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library_name: allennlp
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- text: "Is this review positive or negative? Review: Best cast iron skillet you will ever buy."
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example_title: "Sentiment analysis"
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- text: "Barack Obama nominated Hilary Clinton as his secretary of state on Monday. He chose her because she had ..."
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example_title: "Coreference resolution"
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example_title: "Logic puzzles"
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example_title: "Reading comprehension"
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