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layout-fidnet-v3-layoutdm-publaynet
creative-graphic-design/layout-fidnet-v3-layoutdm-publaynet
layout-fidnet-v3-layoutdm-publaynet is a feature extraction model from creative-graphic-design. Use it when you need embeddings to search or compare text. It is set up for transformers. The card lists the license as mit.
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Model card
From the Hugging Face model README
FIDNetV3 from LayoutDM
from transformers import AutoModel
model = AutoModel.from_pretrained("shunk031/layoutdm-fidnet-v3-publaynet", trust_remote_code=True)
print(model)
# LayoutDmFIDNetV3(
# (emb_label): Embedding(5, 256)
# (fc_bbox): Linear(in_features=4, out_features=256, bias=True)
# (enc_fc_in): Linear(in_features=512, out_features=256, bias=True)
# (enc_transformer): TransformerWithToken(
# (core): TransformerEncoder(
# (layers): ModuleList(
# (0-3): 4 x TransformerEncoderLayer(
# (self_attn): MultiheadAttention(
# (out_proj): NonDynamicallyQuantizableLinear(in_features=256, out_features=256, bias=True)
# )
# (linear1): Linear(in_features=256, out_features=128, bias=True)
# (dropout): Dropout(p=0.1, inplace=False)
# (linear2): Linear(in_features=128, out_features=256, bias=True)
# (norm1): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
# (norm2): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
# (dropout1): Dropout(p=0.1, inplace=False)
# (dropout2): Dropout(p=0.1, inplace=False)
# )
# )
# )
# )
# (fc_out_disc): Linear(in_features=256, out_features=1, bias=True)
# (dec_fc_in): Linear(in_features=512, out_features=256, bias=True)
# (dec_transformer): TransformerEncoder(
# (layers): ModuleList(
# (0-3): 4 x TransformerEncoderLayer(
# (self_attn): MultiheadAttention(
# (out_proj): NonDynamicallyQuantizableLinear(in_features=256, out_features=256, bias=True)
# )
# (linear1): Linear(in_features=256, out_features=128, bias=True)
# (dropout): Dropout(p=0.1, inplace=False)
# (linear2): Linear(in_features=128, out_features=256, bias=True)
# (norm1): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
# (norm2): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
# (dropout1): Dropout(p=0.1, inplace=False)
# (dropout2): Dropout(p=0.1, inplace=False)
# )
# )
# )
# (fc_out_cls): Linear(in_features=256, out_features=5, bias=True)
# (fc_out_bbox): Linear(in_features=256, out_features=4, bias=True)
# )
- Task
- Feature Extraction
- Library
- transformers
- Type
- layoutdm_fidnet_v3
- License
- mit
- Created
- Dec 26, 2023
- Updated
- Jul 28, 2024