Instructions to use marcosfp/distilbert-base-uncased-finetuned-objectivity-rotten with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use marcosfp/distilbert-base-uncased-finetuned-objectivity-rotten with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="marcosfp/distilbert-base-uncased-finetuned-objectivity-rotten")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("marcosfp/distilbert-base-uncased-finetuned-objectivity-rotten") model = AutoModelForSequenceClassification.from_pretrained("marcosfp/distilbert-base-uncased-finetuned-objectivity-rotten") - Notebooks
- Google Colab
- Kaggle
Objectivity sentence classification model based on distilbert-base-uncased-finetuned-sst-2-english. It was fine-tuned with Rotten-IMDB movie review data using extracted sentences from film plots as objective examples and review comments as subjective language examples.
With a test set of 5%, we obtained an accuracy of 96% and f1 of the same value.
Please, feel free to try the demo online with subjective language examples like "I think...", "I believe...", and more objective claims.
For any further comments contact me, at marcosfernandez.pichel@usc.es.
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