https://github.com/praadnya/pre-trained-models

https://github.com/praadnya/pre-trained-models

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• Host: GitHub
• URL: https://github.com/praadnya/pre-trained-models
• Owner: Praadnya
• Created: 2023-12-26T05:02:52.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2024-01-04T10:58:16.000Z (about 1 year ago)
• Last Synced: 2024-11-10T09:09:33.454Z (2 months ago)
• Language: Python
• Size: 32.2 KB
• Stars: 0
• Watchers: 1
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Pre-Trained models 

## TASKS

1. Masked Word Prediction

2. Question Answering

3. Sentiment Analysis

4. Named Entity Recognition

   

## Usage

1. **Install the required libraries:**

   ```bash

   pip install gradio transformers

2. **Run the provided script:**

   ```bash

   python3 script_name.py

3. **Open the provided Gradio interface:**

A Gradio interface will be launched, allowing you to input sentences with placeholders.

The model will perform the respective task using the model API given.

## Example

1. For masked word prediction, if you input the sentence "I am _ hungry," the models will predict the word that best fits in the placeholder.   

2. For question answering, if you give context and question like: 

   Context:

   "The Eiffel Tower is a famous landmark in Paris, France. It was completed in 1889 and has become a symbol of the city."

   Question:

   "What is the Eiffel Tower?"

   It gives an appropriate answer.

3. For Sentiment Analysis, for a given input statement it returns either POSITIVE or NEGATIVE or NEUTRAL label depending on the sentiment of the statement.

4. For named entity recognition, for a given input statement it returns the labels like LOC,PER,ORG,MISC etc depending on the model beig used and thier respective     classes. 

## Dependencies

1. Gradio

2. Transformers

   

## Acknowledgements

1. bert-base-uncased

2. dmis-lab/biobert-large-cased-v1.1-squad

3. roberta-base

4. distilbert-base-cased-distilled-squad

5. VMware/minilmv2-l12-h384-from-roberta-large-mrqa

6. xlnet-base-cased

7. Gradio: https://www.gradio.app/docs/interface