Instructions to use nsugianto/tblstructrecog_tuned_tbltransstrucrecog_noncomplex_complex_conlash_b5_1807s_lr5e5_dec1e4_bs8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nsugianto/tblstructrecog_tuned_tbltransstrucrecog_noncomplex_complex_conlash_b5_1807s_lr5e5_dec1e4_bs8 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="nsugianto/tblstructrecog_tuned_tbltransstrucrecog_noncomplex_complex_conlash_b5_1807s_lr5e5_dec1e4_bs8")# Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("nsugianto/tblstructrecog_tuned_tbltransstrucrecog_noncomplex_complex_conlash_b5_1807s_lr5e5_dec1e4_bs8") model = AutoModelForObjectDetection.from_pretrained("nsugianto/tblstructrecog_tuned_tbltransstrucrecog_noncomplex_complex_conlash_b5_1807s_lr5e5_dec1e4_bs8", device_map="auto") - Notebooks
- Google Colab
- Kaggle
tblstructrecog_tuned_tbltransstrucrecog_noncomplex_complex_conlash_b5_1807s_lr5e5_dec1e4_bs8
This model is a fine-tuned version of nsugianto/tblstructrecog_tuned_tbltransstrucrecog_noncomplex_complex_conlash_b5_1807s_lr1e6_dec1e5_bs4 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 750
- mixed_precision_training: Native AMP
Training results
Framework versions
- Transformers 4.41.0.dev0
- Pytorch 2.0.1
- Datasets 2.18.0
- Tokenizers 0.19.1
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