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
Commit ·
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Parent(s): 2cf40a3
Create README.md
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README.md
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---
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language:
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- en
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library_name: allennlp
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widget:
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- text: "Background: Chronic Depression (CD) significantly impacts individuals' quality of life. Mindfulness-Based Cognitive Therapy (MBCT) presents a potential treatment. Methods: 128 CD patients were randomized to an 8-week MBCT program or a wait-list control group, assessed pre- and post-treatment using the Hamilton Depression Rating Scale (HDRS). Results: Significant reductions in HDRS scores were observed post-treatment in the MBCT group (p<0.001), maintained at 3- and 6-month follow-ups, with large effect size (Cohen’s d=1.32). Conclusion: MBCT is an effective and sustainable treatment for CD, warranting further exploration."
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