metadata

library_name: transformers
tags:
  - intel
  - ipex
  - emotion-recognition
  - sentiment-analysis
  - Beginner Level
license: mit
language:
  - en

model_card_content = """

Model Card for My Sentiment Analysis Bot : Intel Workshop

Model Description

Purpose: For Classifying Emotions from text.
Model architecture: Distilbert model
Training data: Text examples with labels corresponding to emotions such as ex: sad, happy, love, etc.

Intended users: Model created and deployed for learning purposes while following Intel's Huggingface Optimization Workshop.
Use cases: Sentiment Analysis, Social Media Comment Filtering, Review Filtering.

Input: Winning the lottery has filled me with an indescribable joy that makes me want to sing and dance! -- Type of Sentiment: Happy
Input: Saying goodbye to my beloved pet was the hardest thing I've ever done, and the sadness feels like a heavy weight on my heart. -- Type of Sentiment: Sad
Input: The blatant injustice of the situation infuriates me to the core. How can anyone stand by and allow such unfairness? -- Type of Sentiment: Angry
Input: I can't believe it - I actually got the job! This is a complete shock, but an amazing one! -- Type of Sentiment: Surprised
Input: The storm raging outside is terrifying. The howling wind and crashing thunder make me feel uneasy and unsafe. -- Type of Sentiment: Fearful

Known limitations: This deplopyment is done for learning purposes. This is not trained on a big dataset and is not that accurate. Causes erroneous results.

Training Platform: Intel Developer Cloud Training JupyterLabs using Intel 4th Generation Xeon Processors.
Link to IDC Training: https://console.cloud.intel.com/training

Known Optimizations: Outside of my beginner level understanding. I followed tutorial steps.

Ethical concerns: Created for Learning purposes only,may not be monitored, improved. Use with caution.

"""