Image-to-Text
Transformers
PyTorch
Safetensors
vision-encoder-decoder
image-text-to-text
image-captioning
Instructions to use bipin/image-caption-generator with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bipin/image-caption-generator with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "image-to-text" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("image-to-text", model="bipin/image-caption-generator")# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("bipin/image-caption-generator") model = AutoModelForMultimodalLM.from_pretrained("bipin/image-caption-generator") - Notebooks
- Google Colab
- Kaggle
update model card README.md
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README.md
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This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.
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## Model description
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate:
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- train_batch_size: 8
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- eval_batch_size: 8
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- seed: 42
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| Training Loss | Epoch | Step | Validation Loss |
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### Framework versions
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This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.2461
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## Model description
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 5e-05
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- train_batch_size: 8
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- eval_batch_size: 8
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- seed: 42
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| Training Loss | Epoch | Step | Validation Loss |
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| 0.3353 | 1.0 | 809 | 0.2544 |
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| 0.2342 | 2.0 | 1618 | 0.2454 |
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| 0.2099 | 3.0 | 2427 | 0.2461 |
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### Framework versions
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