Hugging Face

Load a pre-trained sentiment analysis model from the Hub.

Input customer reviews or social media posts.

Use the model to classify the sentiment as positive, negative, or neutral.

Aggregate the results to identify overall sentiment trends.

Load a pre-trained text summarization model.

Input the long article or document.

Use the model to generate a concise summary.

Review and refine the summary for accuracy and clarity.

Load a pre-trained image classification model.

Input the image.

Use the model to classify the objects or scenes in the image.

Display the classification results with confidence scores.

Load a pre-trained code generation model.

Input the natural language description of the desired code.

Use the model to generate the corresponding code snippet.

Test and refine the generated code.

Load a pre-trained machine translation model.

Input the text in the source language.

Specify the target language.

Use the model to translate the text.

Review and refine the translated text for accuracy.

Select a suitable pre-trained conversational model from Hugging Face Hub.

Fine-tune the model on a specific domain or use case dataset.

Deploy the fine-tuned model using Inference Endpoints.

Integrate the Inference Endpoint with a chat interface or messaging platform.

Continuously monitor and refine the chatbot's performance based on user feedback.