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--- |
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language: |
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- en |
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license: mit |
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tags: |
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- generated_from_trainer |
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- image-to-text |
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- image-captioning |
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base_model: microsoft/git-base |
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pipeline_tag: image-to-text |
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model-index: |
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- name: git-base-instagram-cap |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# git-base-instagram-cap |
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This model is a fine-tuned version of [microsoft/git-base](https://huggingface.co/microsoft/git-base) on an |
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[mrSoul7766/instagram_post_captions](https://huggingface.co/datasets/mrSoul7766/instagram_post_captions). |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0093 |
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### Usage |
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you can leverage the capabilities provided by the Hugging Face Transformers library. Here's a basic example using Python: |
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```python |
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# Use a pipeline as a high-level helper |
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from transformers import pipeline |
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pipe = pipeline("image-to-text", model="mrSoul7766/git-base-instagram-cap") |
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# Generate caption |
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caption = pipe("/content/download (12).png",max_new_tokens =100) |
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# Print the generated answer |
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print(caption[0]['generated_text']) |
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``` |
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``` |
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i love my blonde character in kim kardashian hollywood! i'm playing now who's playing with me? |
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``` |
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### Framework versions |
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- Transformers 4.37.0.dev0 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.16.1 |
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- Tokenizers 0.15.0 |
