Instructions to use snsf-data/specter2-review-negative with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use snsf-data/specter2-review-negative with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="snsf-data/specter2-review-negative")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("snsf-data/specter2-review-negative") model = AutoModelForSequenceClassification.from_pretrained("snsf-data/specter2-review-negative", device_map="auto") - Notebooks
- Google Colab
- Kaggle
updating model card
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README.md
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# prediction for an example review sentence not addressing negative statement
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classification_pipeline("There are currently several activities on an international level that have identified the issue and activities are underway.")
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```
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## Citation
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# prediction for an example review sentence not addressing negative statement
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classification_pipeline("There are currently several activities on an international level that have identified the issue and activities are underway.")
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```
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## Model Limitations
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- *Human Assessment Required*: This model should not be used for automatic classification of grant peer review reports without human oversight.
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- *Limited Training Data*: The model was fine-tuned on a limited sample of 2,500 annotated sentences. Therefore, its classification accuracy should be critically evaluated before deployment.
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- *Specific Training Data*: The training data consists of a random sample of SNSF grant peer review reports. As such, the model's external validity to other datasets may be limited.
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## Citation
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