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BlipFineTuneHacklytics2024

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metadata
license: apache-2.0
language:
  - en
metrics:
  - bleu
library_name: transformers
pipeline_tag: image-to-text

Model Card for Model ID

This model is for the Assurant Challenge 1.

Model Details

This is a BLIP Model that has been fine-tuned for 30 epochs using a custom data scrapped for web. It has been finetuned using a dataset which is a collection of (text description of a scene, collection of images of that scene). The underlying application is to assist the insurance officer in verifying and approving the house rental damage claims raised by the user, and make predictions of future problems that might appear and general advice on maintaining the house.

Model Description

The architecture is exactly the same as BLIP.

  • Developed by: Krishna Sri Ipsit Mantri, Varnica Chabria, Pavan Chaitanya Penagamuri, Kalyan Salkar
  • Funded by [optional]: Used Intel Developer Cloud Credits provided for Hacklytics2024
  • Shared by [optional]:
  • Model type: Fine-tuned image-to-text model
  • Language(s) (NLP): English
  • License: Apache 2.0
  • Finetuned from model [optional]: BLIP

Model Sources [optional]

  • Repository: [More Information Needed]
  • Paper [optional]: [More Information Needed]
  • Demo [optional]: [More Information Needed]

Uses

Direct Use

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Downstream Use [optional]

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Out-of-Scope Use

Should not be used for anything other than the challenge.

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Bias, Risks, and Limitations

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Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

How to Get Started with the Model

Use the code below to get started with the model.

[More Information Needed]

Training Details

Training Data

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Training Procedure

Preprocessing [optional]

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Training Hyperparameters

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Speeds, Sizes, Times [optional]

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Evaluation

Testing Data, Factors & Metrics

Testing Data

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Factors

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Metrics

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Results

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Summary

Model Examination [optional]

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Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

  • Hardware Type: [More Information Needed]
  • Hours used: [More Information Needed]
  • Cloud Provider: Intel
  • Compute Region: [More Information Needed]
  • Carbon Emitted: [More Information Needed]

Technical Specifications [optional]

Model Architecture and Objective

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Compute Infrastructure

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Hardware

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Software

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Citation [optional]

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APA:

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