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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Update README.md
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README.md
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@@ -17,7 +17,7 @@ The SciBERT model was validated against one in-domain and two out-of-domain data
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## Using the Model on Huggingface
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The model can be used on Hugginface utilizing the "Hosted inference API" in the window on the right.
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Click 'Compute' to predict the class labels example abstract or an abstract inserted by yourself.
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The class label 'positive' corresponds to 'positive results only', while 'negative' represents 'mixed and negative results'.
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## Using the Model on Huggingface
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The model can be used on Hugginface utilizing the "Hosted inference API" in the window on the right.
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Click 'Compute' to predict the class labels for an example abstract or an abstract inserted by yourself.
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The class label 'positive' corresponds to 'positive results only', while 'negative' represents 'mixed and negative results'.
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