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
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## Model
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SciBERT text classification model for positive and negative results prediction in scientific abstracts of clinical psychology and psychotherapy. The preprint
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## Data
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We annotated over 1,900 clinical psychology abstracts into two categories: 'positive results only' and 'mixed or negative results', and trained models using SciBERT.
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## Model
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SciBERT text classification model for positive and negative results prediction in scientific abstracts of clinical psychology and psychotherapy. The preprint "Classifying Positive Results in Clinical Psychology Using Natural Language Processing" by Louis Schiekiera, Helen Niemeyer & Jonathan Diederichs is available on [PsyArxiv](https://osf.io/preprints/psyarxiv/uxyzh).
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## Data
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We annotated over 1,900 clinical psychology abstracts into two categories: 'positive results only' and 'mixed or negative results', and trained models using SciBERT.
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