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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**Figure 1** <br>
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*Comparing model performances across in-domain and out-of-domain data; Colored bars represent different model types; Samples: `MAIN` test: n = 198 abstracts; `VAL1`: n = 150 abstracts; `VAL2`: n = 150 abstracts.*
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Further information on analyzing your own or our example data can be found in this [script](https://github.com/
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from our [GitHub repository](https://github.com/
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## Disclaimer
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**Figure 1** <br>
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*Comparing model performances across in-domain and out-of-domain data; Colored bars represent different model types; Samples: `MAIN` test: n = 198 abstracts; `VAL1`: n = 150 abstracts; `VAL2`: n = 150 abstracts.*
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<br>
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```
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Further information on analyzing your own or our example data can be found in this [script](https://github.com/schiekiera/NegativeResultDetector/blob/main/Scripts/example_folder/Predict_Example_Abstracts_using_NegativeResultDetector.ipynb)
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from our [GitHub repository](https://github.com/schiekiera/NegativeResultDetector).
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## Disclaimer
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