File size: 1,388 Bytes
8c02470 f5993e4 8c02470 db2fa5c f5993e4 8c02470 8fa9ece 8c02470 c16a918 8c02470 8fa9ece 313fa49 8fa9ece 8c02470 8859e1f |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 |
---
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 |