Instructions to use spentaur/yelp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use spentaur/yelp with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="spentaur/yelp")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("spentaur/yelp") model = AutoModelForMaskedLM.from_pretrained("spentaur/yelp", device_map="auto") - Notebooks
- Google Colab
- Kaggle
YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
DistilBERT Yelp Review Sentiment
This model is used for sentiment analysis on english yelp reviews.
It is a DistilBERT model trained on 1 million reviews from the yelp open dataset.
It is a regression model, with outputs in the range of ~-2 to ~2. With -2 being 1 star and 2 being 5 stars.
It was trained using the ktrain because of it's ease of use.
Example use:
tokenizer = AutoTokenizer.from_pretrained(
'distilbert-base-uncased', use_fast=True)
model = TFAutoModelForSequenceClassification.from_pretrained(
"spentaur/yelp")
review = "This place is great!"
input_ids = tokenizer.encode(review, return_tensors='tf')
pred = model(input_ids)[0][0][0].numpy()
# pred should === 1.9562385
- Downloads last month
- 4