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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## Model
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SciBERT text classification model for positive and negative results prediction in scientific abstracts of clinical psychology and psychotherapy.
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The preprint "Classifying Positive Results in Clinical Psychology Using Natural Language Processing" by Louis Schiekiera, Helen Niemeyer & Jonathan
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Diederichs is available on
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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.
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The preprint "Classifying Positive Results in Clinical Psychology Using Natural Language Processing" by Louis Schiekiera, Helen Niemeyer & Jonathan
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Diederichs is available on [PsyArxiv](https://osf.io/preprints/psyarxiv/uxyzh){:target="_blank"}.
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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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