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senga-ml
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donut-v16

Image-Text-to-Text
Transformers
Safetensors
vision-encoder-decoder
Inference Endpoints
Model card Files Community
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  1. README.md +199 -0
  2. added_tokens.json +104 -0
  3. preprocessor_config.json +44 -0
  4. sentencepiece.bpe.model +3 -0
  5. special_tokens_map.json +55 -0
  6. tokenizer.json +0 -0
  7. tokenizer_config.json +883 -0
README.md ADDED
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+ ---
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+ library_name: transformers
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+ tags: []
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+ ---
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+
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+ # Model Card for Model ID
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+
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+ <!-- Provide a quick summary of what the model is/does. -->
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+
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+
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+
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+ ## Model Details
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+
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+ ### Model Description
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+ <!-- Provide a longer summary of what this model is. -->
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+ This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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+ ## Uses
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+ <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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+ Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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+ ## How to Get Started with the Model
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+ Use the code below to get started with the model.
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+ [More Information Needed]
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+ ## Training Details
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+ #### Preprocessing [optional]
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+ [More Information Needed]
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+ - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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+ <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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+ [More Information Needed]
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+ ## Evaluation
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+ ### Results
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+ #### Summary
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+ ## Model Examination [optional]
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+ <!-- Relevant interpretability work for the model goes here -->
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+ ## Environmental Impact
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+ Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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+ - **Hardware Type:** [More Information Needed]
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+ - **Cloud Provider:** [More Information Needed]
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+ - **Compute Region:** [More Information Needed]
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+ - **Carbon Emitted:** [More Information Needed]
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+
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+ ## Technical Specifications [optional]
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+
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+ ### Model Architecture and Objective
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+ ## Glossary [optional]
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+ <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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+ [More Information Needed]
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+ ## More Information [optional]
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+ [More Information Needed]
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+ ## Model Card Authors [optional]
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+ ## Model Card Contact
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+ [More Information Needed]
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107
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108
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109
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110
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111
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112
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113
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114
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115
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116
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117
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118
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119
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120
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121
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122
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123
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124
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126
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127
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128
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129
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131
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132
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135
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136
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137
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140
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143
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144
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147
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148
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149
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151
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152
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153
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154
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156
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159
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160
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161
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163
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164
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165
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166
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167
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168
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169
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171
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172
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175
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176
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180
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181
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182
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183
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184
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185
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191
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192
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196
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199
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200
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201
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204
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207
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208
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212
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214
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215
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216
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217
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220
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224
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225
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228
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231
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232
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233
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236
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239
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240
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244
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260
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263
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264
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271
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272
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276
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280
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284
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287
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288
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295
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296
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300
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319
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327
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328
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335
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336
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340
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343
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344
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345
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360
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409
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415
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420
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456
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471
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472
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473
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479
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480
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505
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512
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527
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528
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529
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531
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532
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533
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535
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536
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537
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539
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542
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543
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544
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545
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550
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551
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552
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553
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556
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559
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560
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561
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567
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568
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569
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575
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576
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580
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582
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583
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584
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585
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591
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592
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593
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621
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623
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632
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648
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652
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653
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655
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656
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657
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664
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665
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668
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669
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670
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671
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672
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673
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681
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689
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696
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700
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701
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703
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704
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705
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711
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712
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713
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715
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716
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717
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718
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719
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720
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721
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722
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723
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724
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725
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726
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727
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728
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729
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730
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731
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733
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735
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736
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737
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738
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740
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744
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745
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748
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751
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752
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753
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754
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760
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761
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762
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764
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768
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769
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770
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772
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775
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776
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777
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778
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780
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781
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784
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785
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792
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793
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796
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797
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799
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800
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801
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802
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803
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804
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805
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806
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807
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808
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809
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810
+ },
811
+ "57618": {
812
+ "content": "<s_delivery_contact_person>",
813
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814
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815
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816
+ "single_word": false,
817
+ "special": false
818
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819
+ "57619": {
820
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821
+ "lstrip": false,
822
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823
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824
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825
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826
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827
+ "57620": {
828
+ "content": "<s_item_hscode>",
829
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830
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831
+ "rstrip": false,
832
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833
+ "special": false
834
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835
+ "57621": {
836
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837
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838
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839
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840
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841
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842
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843
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844
+ "content": "<s_item_uom>",
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846
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847
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848
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849
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851
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852
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857
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+ }
859
+ },
860
+ "additional_special_tokens": [
861
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862
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863
+ ],
864
+ "bos_token": "<s>",
865
+ "clean_up_tokenization_spaces": true,
866
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867
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868
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870
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871
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872
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873
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874
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875
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876
+ "sep_token": "</s>",
877
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878
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879
+ "tokenizer_class": "XLMRobertaTokenizer",
880
+ "truncation_side": "right",
881
+ "truncation_strategy": "longest_first",
882
+ "unk_token": "<unk>"
883
+ }