Instructions to use luchaf/table-transformer-structure-recognition-pointless with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use luchaf/table-transformer-structure-recognition-pointless with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="luchaf/table-transformer-structure-recognition-pointless")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("luchaf/table-transformer-structure-recognition-pointless") model = AutoModelForObjectDetection.from_pretrained("luchaf/table-transformer-structure-recognition-pointless", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Delete config.json
Browse files- config.json +0 -59
config.json
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{
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"activation_dropout": 0.0,
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"activation_function": "relu",
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"architectures": [
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"TableTransformerForObjectDetection"
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],
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"attention_dropout": 0.0,
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"auxiliary_loss": false,
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"backbone": "resnet18",
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"bbox_cost": 5,
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"bbox_loss_coefficient": 5,
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"ce_loss_coefficient": 1,
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"class_cost": 1,
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"d_model": 256,
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"decoder_attention_heads": 8,
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"decoder_ffn_dim": 2048,
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"decoder_layerdrop": 0.0,
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"decoder_layers": 6,
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"dice_loss_coefficient": 1,
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"dilation": false,
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"dropout": 0.1,
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"encoder_attention_heads": 8,
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"encoder_ffn_dim": 2048,
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"encoder_layerdrop": 0.0,
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"encoder_layers": 6,
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"eos_coefficient": 0.4,
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"giou_cost": 2,
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"giou_loss_coefficient": 2,
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"id2label": {
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"0": "table",
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"1": "table row",
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"2": "table column",
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"3": "table column header",
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"4": "table projected row header",
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"5": "table spanning cell"
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},
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"init_std": 0.02,
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"init_xavier_std": 1.0,
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"is_encoder_decoder": true,
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"label2id": {
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"table": 0,
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"table column": 1,
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"table column header": 3,
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"table projected row header": 4,
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"table row": 2,
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"table spanning cell": 5
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},
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"mask_loss_coefficient": 1,
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"max_position_embeddings": 1024,
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"model_type": "table-transformer",
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"num_channels": 3,
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"num_hidden_layers": 6,
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"num_queries": 125,
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"position_embedding_type": "sine",
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"scale_embedding": false,
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"torch_dtype": "float32",
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"transformers_version": "4.24.0.dev0",
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"use_pretrained_backbone": true
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}
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