Instructions to use AmineAllo/table-transformer-azure-dust-65 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AmineAllo/table-transformer-azure-dust-65 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="AmineAllo/table-transformer-azure-dust-65")# Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("AmineAllo/table-transformer-azure-dust-65") model = AutoModelForObjectDetection.from_pretrained("AmineAllo/table-transformer-azure-dust-65", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from AmineAllo/table-transformer-azure-dust-65: direct link, hf CLI and curl.
- Browser
- Download file 1.37 kB
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https://huggingface.co/AmineAllo/table-transformer-azure-dust-65/resolve/main/README.md
- Command line
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hf download hf://AmineAllo/table-transformer-azure-dust-65/README.md
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curl -L -o README.md https://huggingface.co/AmineAllo/table-transformer-azure-dust-65/resolve/main/README.md
1.37 kB
metadata
base_model: toobiza/MT-celestial-grass-58
tags:
- generated_from_trainer
model-index:
- name: table-transformer-azure-dust-65
results: []
table-transformer-azure-dust-65
This model is a fine-tuned version of toobiza/MT-celestial-grass-58 on an unknown dataset. It achieves the following results on the evaluation set:
- eval_loss: 1.1439
- eval_loss_ce: 0.0014
- eval_loss_bbox: 0.0818
- eval_cardinality_error: 1.0
- eval_giou: 92.6646
- eval_runtime: 26.1327
- eval_samples_per_second: 3.061
- eval_steps_per_second: 1.531
- epoch: 4.17
- step: 350
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: 5e-05
- train_batch_size: 4
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3