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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from ClinicalMetaScience/NegativeResultDetector: direct link, hf CLI and curl.
- Browser
- Download file 440 MB
-
https://huggingface.co/ClinicalMetaScience/NegativeResultDetector/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://ClinicalMetaScience/NegativeResultDetector/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/ClinicalMetaScience/NegativeResultDetector/resolve/main/pytorch_model.bin
440 MB
- Xet hash:
- fd2aea9d2d0511de18a21cce4459c48ac135eea014b1106407a65f5d9ff911a6
- Size of remote file:
- 440 MB
- SHA256:
- db30be11ef36436f5d90e4e43856a5733a97bbff0cb47fc54bc6843a0664ea12
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