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")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ClinicalMetaScience/NegativeResultDetector") model = AutoModelForSequenceClassification.from_pretrained("ClinicalMetaScience/NegativeResultDetector") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2ed488b225f8217d671964a90c3fa57cdffd75d3e23659be86e83c46029e17b6
- Size of remote file:
- 4.03 kB
- SHA256:
- 17960aec5420fa9c7fc9721c91172afe92418551816228a56149732bb59ed79b
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