Instructions to use copenlu/scientific-exaggeration-detection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use copenlu/scientific-exaggeration-detection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="copenlu/scientific-exaggeration-detection")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("copenlu/scientific-exaggeration-detection") model = AutoModelForSequenceClassification.from_pretrained("copenlu/scientific-exaggeration-detection") - Notebooks
- Google Colab
- Kaggle
Add link to training dataset
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# Scientific Exaggeration Detector
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This is the best exaggeration detection model from the paper "Semi-Supervised Exaggeration Detection of Health Science Press Releases" in EMNLP 2021. The model can be used to measure the causal claim strength of a scientific sentence, which can be used to compare two sentences for exaggeration in causal claim strength. Please use the following when citing this work:
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publisher = {Association for Computational Linguistics},
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year = 2021
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}
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```
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---
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datasets:
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- copenlu/scientific-exaggeration-detection
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---
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# Scientific Exaggeration Detector
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This is the best exaggeration detection model from the paper "Semi-Supervised Exaggeration Detection of Health Science Press Releases" in EMNLP 2021. The model can be used to measure the causal claim strength of a scientific sentence, which can be used to compare two sentences for exaggeration in causal claim strength. Please use the following when citing this work:
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publisher = {Association for Computational Linguistics},
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year = 2021
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}
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```
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