metadata
library_name: transformers
datasets:
- Yelp/yelp_review_full
language:
- en
metrics:
- accuracy
base_model:
- tabularisai/multilingual-sentiment-analysis
model-index:
- name: Caelen
results:
- task:
type: text-classification
dataset:
type: text-classification
name: Yelp/yelp_review_full
metrics:
- name: accuracy
type: text-classification
value: 0.6772
verified: false
This model takes the text from a Yelp review and predicts a label corresponding to the number of stars for the review. The labels are:
- "Very Negative" = 1 star
- "Negative" = 2 stars
- "Neutral" = 3 stars
- "Positive" = 4 stars
- "Very Positive" = 5 stars
Example Inference Code:
from transformers import AutoTokenizer, pipeline
# Load the base model tokenizer
tokenizer = AutoTokenizer.from_pretrained('tabularisai/multilingual-sentiment-analysis')
# Load the classification pipeline with the specified model
pipe = pipeline("text-classification", model="Neleac/yelp-review-classifier", tokenizer=tokenizer)
# Classify a new Yelp review
review = "This is by far my favorite Panera location in the Pittsburgh area. \
Friendly, plenty of room to sit, and good quality food & coffee. \
Panera is a great place to hang out and read the news - they even have free WiFi! \
Try their toasted sandwiches, especially the chicken bacon dijon."
result = pipe(review)
# Print the result
print(result) # [{'label': 'Very Positive', 'score': 0.7158929109573364}]