Instructions to use pedro1111/layout2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pedro1111/layout2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("document-question-answering", model="pedro1111/layout2")# Load model directly from transformers import AutoProcessor, AutoModelForDocumentQuestionAnswering processor = AutoProcessor.from_pretrained("pedro1111/layout2") model = AutoModelForDocumentQuestionAnswering.from_pretrained("pedro1111/layout2") - Notebooks
- Google Colab
- Kaggle
File size: 2,756 Bytes
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"_name_or_path": "microsoft/layoutlmv2-base-uncased",
"architectures": [
"LayoutLMv2ForQuestionAnswering"
],
"attention_probs_dropout_prob": 0.1,
"convert_sync_batchnorm": true,
"coordinate_size": 128,
"detectron2_config_args": {
"MODEL.ANCHOR_GENERATOR.SIZES": [
[
32
],
[
64
],
[
128
],
[
256
],
[
512
]
],
"MODEL.BACKBONE.NAME": "build_resnet_fpn_backbone",
"MODEL.FPN.IN_FEATURES": [
"res2",
"res3",
"res4",
"res5"
],
"MODEL.MASK_ON": true,
"MODEL.PIXEL_STD": [
57.375,
57.12,
58.395
],
"MODEL.POST_NMS_TOPK_TEST": 1000,
"MODEL.RESNETS.ASPECT_RATIOS": [
[
0.5,
1.0,
2.0
]
],
"MODEL.RESNETS.DEPTH": 101,
"MODEL.RESNETS.NUM_GROUPS": 32,
"MODEL.RESNETS.OUT_FEATURES": [
"res2",
"res3",
"res4",
"res5"
],
"MODEL.RESNETS.SIZES": [
[
32
],
[
64
],
[
128
],
[
256
],
[
512
]
],
"MODEL.RESNETS.STRIDE_IN_1X1": false,
"MODEL.RESNETS.WIDTH_PER_GROUP": 8,
"MODEL.ROI_BOX_HEAD.NAME": "FastRCNNConvFCHead",
"MODEL.ROI_BOX_HEAD.NUM_FC": 2,
"MODEL.ROI_BOX_HEAD.POOLER_RESOLUTION": 14,
"MODEL.ROI_HEADS.IN_FEATURES": [
"p2",
"p3",
"p4",
"p5"
],
"MODEL.ROI_HEADS.NAME": "StandardROIHeads",
"MODEL.ROI_HEADS.NUM_CLASSES": 5,
"MODEL.ROI_MASK_HEAD.NAME": "MaskRCNNConvUpsampleHead",
"MODEL.ROI_MASK_HEAD.NUM_CONV": 4,
"MODEL.ROI_MASK_HEAD.POOLER_RESOLUTION": 7,
"MODEL.RPN.IN_FEATURES": [
"p2",
"p3",
"p4",
"p5",
"p6"
],
"MODEL.RPN.POST_NMS_TOPK_TRAIN": 1000,
"MODEL.RPN.PRE_NMS_TOPK_TEST": 1000,
"MODEL.RPN.PRE_NMS_TOPK_TRAIN": 2000
},
"fast_qkv": true,
"gradient_checkpointing": false,
"has_relative_attention_bias": true,
"has_spatial_attention_bias": true,
"has_visual_segment_embedding": true,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 768,
"image_feature_pool_shape": [
7,
7,
256
],
"initializer_range": 0.02,
"intermediate_size": 3072,
"layer_norm_eps": 1e-12,
"max_2d_position_embeddings": 1024,
"max_position_embeddings": 512,
"max_rel_2d_pos": 256,
"max_rel_pos": 128,
"model_type": "layoutlmv2",
"num_attention_heads": 12,
"num_hidden_layers": 12,
"output_past": true,
"pad_token_id": 0,
"rel_2d_pos_bins": 64,
"rel_pos_bins": 32,
"shape_size": 128,
"torch_dtype": "float32",
"transformers_version": "4.32.0",
"type_vocab_size": 2,
"vocab_size": 30522
}
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