review-LLMs
Collection
Collection of encoder-only LLMs with extended pre-training in the domain of mobile app reviews.
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6 items
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Updated
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3
This model is a fine-tuned version of xlnet-large-cased
on a large dataset of mobile app reviews. The model is designed to understand and process text from mobile app reviews, providing enhanced performance for tasks such as feature extraction, sentiment analysis, and review summarization from app reviews.
xlnet-large-cased
The extended pre-training was performed using a diverse dataset of mobile app reviews collected from various app stores. The dataset includes reviews of different lengths, sentiments, and topics, providing a robust foundation for understanding the nuances of mobile app user feedback.
The model was fine-tuned using the following parameters:
from transformers import XLNetTokenizer, XLNetForSequenceClassification
tokenizer = XLNetTokenizer.from_pretrained('quim-motger/reviewXLNet-large-cased')
model = XLNetForSequenceClassification.from_pretrained('quim-motger/reviewXLNet-large-cased')
from transformers import pipeline
nlp = pipeline('sentiment-analysis', model=model, tokenizer=tokenizer)
review = "This app is fantastic! I love the user-friendly interface and features."
result = nlp(review)
print(result)
# Output: [{'label': 'POSITIVE', 'score': 0.98}]
from transformers import pipeline
summarizer = pipeline('summarization', model=model, tokenizer=tokenizer)
long_review = "I have been using this app for a while and it has significantly improved my productivity.
The range of features is excellent, and the user interface is intuitive. However, there are occasional
bugs that need fixing."
summary = summarizer(long_review, max_length=50, min_length=25, do_sample=False)
print(summary)
# Output: [{'summary_text': 'The app has significantly improved my productivity with its excellent features and intuitive user interface. However, occasional bugs need fixing.'}]