# Sentiment Analysis
For an university project at the University of Applied Sciences Kiel, we conducted a sentiment analysis with the aim of classifying restaurant ratings. The model of "nlptown/bert-base-multilingual-uncased-sentiment" was used as a pre-trained transformer. As a starting value, an accuracy of 63% was already achieved. Based on this, the transformer was fine-tuned to yelp ratings from Hamburg. After that, an accuracy of 82% was achieved. 


{}
transformers pytorch bert text-classification autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us 

# Sentiment Analysis
For an university project at the University of Applied Sciences Kiel, we conducted a sentiment analysis with the aim of classifying restaurant ratings. The model of "nlptown/bert-base-multilingual-uncased-sentiment" was used as a pre-trained transformer. As a starting value, an accuracy of 63% was already achieved. Based on this, the transformer was fine-tuned to yelp ratings from Hamburg. After that, an accuracy of 82% was achieved. 


# Sentiment Analysis
For an university project at the University of Applied Sciences Kiel, we conducted a sentiment analysis with the aim of classifying restaurant ratings. The model of "nlptown/bert-base-multilingual-uncased-sentiment" was used as a pre-trained transformer. As a starting value, an accuracy of 63% was already achieved. Based on this, the transformer was fine-tuned to yelp ratings from Hamburg. After that, an accuracy of 82% was achieved.
TAGS
#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us 
 # Sentiment Analysis
For an university project at the University of Applied Sciences Kiel, we conducted a sentiment analysis with the aim of classifying restaurant ratings. The model of "nlptown/bert-base-multilingual-uncased-sentiment" was used as a pre-trained transformer. As a starting value, an accuracy of 63% was already achieved. Based on this, the transformer was fine-tuned to yelp ratings from Hamburg. After that, an accuracy of 82% was achieved.
passage: TAGS
#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us 
# Sentiment Analysis
For an university project at the University of Applied Sciences Kiel, we conducted a sentiment analysis with the aim of classifying restaurant ratings. The model of "nlptown/bert-base-multilingual-uncased-sentiment" was used as a pre-trained transformer. As a starting value, an accuracy of 63% was already achieved. Based on this, the transformer was fine-tuned to yelp ratings from Hamburg. After that, an accuracy of 82% was achieved.