---
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: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# git-base-instagram-cap

This model is a fine-tuned version of [microsoft/git-base](https://huggingface.co/microsoft/git-base) on an 
[mrSoul7766/instagram_post_captions](https://huggingface.co/datasets/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:

```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