metadata

language:
  - en
license: mit
tags:
  - generated_from_trainer
  - image-to-text
  - image-captioning
base_model: microsoft/git-base
pipeline_tag: image-to-text
model-index:
  - name: git-base-instagram-cap
    results: []

git-base-instagram-cap

This model is a fine-tuned version of microsoft/git-base on an mrSoul7766/instagram_post_captions.

It achieves the following results on the evaluation set:

Loss: 0.0093

Usage

you can leverage the capabilities provided by the Hugging Face Transformers library. Here's a basic example using Python:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-to-text", model="mrSoul7766/git-base-instagram-cap")

# Generate caption
caption = pipe("/content/download (12).png",max_new_tokens =100)

# Print the generated answer
print(caption[0]['generated_text'])

i love my blonde character in kim kardashian hollywood! i'm playing now who's playing with me?

Framework versions

Transformers 4.37.0.dev0
Pytorch 2.1.0+cu121
Datasets 2.16.1
Tokenizers 0.15.0