What is Sentiment analysis in transformers?
This recipe explains what is Sentiment analysis in transformers.
Recipe Objective - What is Sentiment analysis in transformers?
Sequence classification is the task of classifying sequences according to a specified number of classes. An example of sequence classification is the GLUE dataset, which is completely dependent on the task. If you want to fit the model in the GLUE sequence classification task, you can use the run_glue.py, run_tf_glue.py, run_tf_text_classification.py or run_xnli.py script.
Explore Interesting IoT Project Ideas for Practice
For more related projects -
/projects/data-science-projects/neural-network-projects
/projects/data-science-projects/tensorflow-projects
Example of using a pipeline to perform sentiment analysis: identifying if a sequence is positive or negative.
# Importing libraries
from transformers import pipeline
# Creating classifier using pipeline for sentiment-analysis
sent_classifier_model = pipeline("sentiment-analysis")
# Passing input to classifier
result = sent_classifier_model("No one in your team has been able to solve my problem.")[0]
# Printing result
print("Label: {}, Score: {}".format(result['label'],round(result['score'], 4)))
Output - Label: NEGATIVE, Score: 0.9997
In this way, we can perform sentiment analysis in transformers.
What Users are saying..
Abhinav Agarwal
I come from Northwestern University, which is ranked 9th in the US. Although the high-quality academics at school taught me all the basics I needed, obtaining practical experience was a challenge.... Read More