Image-to-Text
Transformers
PyTorch
TensorBoard
vision-encoder-decoder
image-text-to-text
Generated from Trainer
Instructions to use larabe/testt1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use larabe/testt1 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="larabe/testt1")# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("larabe/testt1") model = AutoModelForMultimodalLM.from_pretrained("larabe/testt1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| license: mit | |
| tags: | |
| - generated_from_trainer | |
| model-index: | |
| - name: testt1 | |
| results: [] | |
| pipeline_tag: image-to-text | |
| <!-- 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. --> | |
| # testt1 | |
| This model is a fine-tuned version of [naver-clova-ix/donut-base](https://huggingface.co/naver-clova-ix/donut-base) on an unknown dataset. | |
| ## Model description | |
| More information needed | |
| ## Intended uses & limitations | |
| More information needed | |
| ## Training and evaluation data | |
| More information needed | |
| ## Training procedure | |
| ### Training hyperparameters | |
| The following hyperparameters were used during training: | |
| - learning_rate: 2e-05 | |
| - train_batch_size: 4 | |
| - eval_batch_size: 8 | |
| - seed: 42 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: linear | |
| - num_epochs: 25 | |
| ### Training results | |
| ### Framework versions | |
| - Transformers 4.30.1 | |
| - Pytorch 2.0.1+cu117 | |
| - Datasets 2.14.0 | |
| - Tokenizers 0.13.3 |