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Update README.md

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  ---
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  license: apache-2.0
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  language:
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- - en
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  widget:
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- - text: "We work with political parties, investors, media organisations, think tanks, NGOs and companies of all sizes around the world to help them understand public opinion, how it affects them, and what they should do in response to it. "
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- - text: "5+ years of experience in data analysis, data science, decision science, or similar quantitative fields, applying experimentation methods to test various hypotheses for customer segmentation, consumer sentiment or perception, and outbound online marketing campaign evaluation"
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- - text: "We offer a competitive salaries based on candidate's qualifications. We also offers three weeks paid vacation per year, paid holidays, a 401(k) plan with employee matching funds, a discretionary bonus and an overall comprehensive benefits package."
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
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  # JoBert
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  inference_prediction = torch.argmax(inference_logits).item()
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  inference_label_name = label_names[inference_prediction]
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- print(f"Inference Result: Predicted Label - {inference_label_name}")
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-
 
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  ---
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  license: apache-2.0
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  language:
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+ - en
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  widget:
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+ - text: >-
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+ We work with political parties, investors, media organisations, think tanks,
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+ NGOs and companies of all sizes around the world to help them understand
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+ public opinion, how it affects them, and what they should do in response to
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+ it.
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+ - text: >-
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+ 5+ years of experience in data analysis, data science, decision science, or
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+ similar quantitative fields, applying experimentation methods to test
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+ various hypotheses for customer segmentation, consumer sentiment or
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+ perception, and outbound online marketing campaign evaluation
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+ - text: >-
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+ We offer a competitive salaries based on candidate's qualifications. We also
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+ offers three weeks paid vacation per year, paid holidays, a 401(k) plan with
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+ employee matching funds, a discretionary bonus and an overall comprehensive
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+ benefits package.
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+ pipeline_tag: text-classification
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  ---
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  # JoBert
 
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  inference_prediction = torch.argmax(inference_logits).item()
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  inference_label_name = label_names[inference_prediction]
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+ print(f"Inference Result: Predicted Label - {inference_label_name}")