# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("dariadaria/reviews_classifier")
model = AutoModelForSequenceClassification.from_pretrained("dariadaria/reviews_classifier", device_map="auto")Quick Links
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SENTIMENT_LABELS = { 'NEGATIVE': 0, 'POSITIVE': 1, 'NEUTRAL': 2, }
num_classes = 3
language:
- en
thumbnail: "url to a thumbnail used in social sharing"
tags:
- sentiment analysis
license: commercial use disallowed
datasets:
- dariadaria/disneyland_reviews
metrics: confusion matrix | | pred:0(NEG)| pred:1(POS)| pred:2(NEU)| | ------------ | --------- | ----------- | ----------- | | true:0(NEG) | 793| 188| 171| | true:1(POS) | 227 | 920| 260| | true:2(NEU) | 115 | 203| 5087|
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="dariadaria/reviews_classifier")