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Masked Word Prediction\n2. Question Answering\n3. Sentiment Analysis\n4. Named Entity Recognition\n   \n## Usage\n\n1. **Install the required libraries:**\n\n   ```bash\n   pip install gradio transformers\n\n2. **Run the provided script:**\n   ```bash\n   python3 script_name.py\n\n3. **Open the provided Gradio interface:**\nA Gradio interface will be launched, allowing you to input sentences with placeholders.\nThe model will perform the respective task using the model API given.\n## Example\n1. For masked word prediction, if you input the sentence \"I am _ hungry,\" the models will predict the word that best fits in the placeholder.   \n2. For question answering, if you give context and question like: \n   Context:\n   \"The Eiffel Tower is a famous landmark in Paris, France. It was completed in 1889 and has become a symbol of the city.\"\n   Question:\n   \"What is the Eiffel Tower?\"\n   It gives an appropriate answer.\n3. For Sentiment Analysis, for a given input statement it returns either POSITIVE or NEGATIVE or NEUTRAL label depending on the sentiment of the statement.\n4. 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. \n\n\n## Dependencies\n1. Gradio\n2. Transformers\n   \n## Acknowledgements\n1. bert-base-uncased\n2. dmis-lab/biobert-large-cased-v1.1-squad\n3. roberta-base\n4. distilbert-base-cased-distilled-squad\n5. VMware/minilmv2-l12-h384-from-roberta-large-mrqa\n6. xlnet-base-cased\n7. Gradio: https://www.gradio.app/docs/interface\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpraadnya%2Fpre-trained-models","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fpraadnya%2Fpre-trained-models","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpraadnya%2Fpre-trained-models/lists"}