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Add onnx-djl module: DistilBERT sentiment with ONNX Runtime and DJL, batching and a Spring Boot endpoint with measured latency

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Co-Authored-By: Claude Sonnet 5.5 <[email protected]>
Claude-Session: https://claude.ai/code/session_01G8ikz8xdWuTP5yun8DZ1hk
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Claude
2026-10-11 09:33:36 +00:00
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onnx-djl/output/01-model-and-tokenizer.txt
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Model: distilbert-base-uncased-finetuned-sst-2-english, ONNX export, 267 MB
ONNX Runtime 1.31.0
Inputs of the ONNX graph:
input_ids TensorInfo(javaType=INT64,onnxType=ONNX_TENSOR_ELEMENT_DATA_TYPE_INT64,shape=[-1, -1],dimNames=[batch_size,sequence_length])
attention_mask TensorInfo(javaType=INT64,onnxType=ONNX_TENSOR_ELEMENT_DATA_TYPE_INT64,shape=[-1, -1],dimNames=[batch_size,sequence_length])
Outputs:
logits TensorInfo(javaType=FLOAT,onnxType=ONNX_TENSOR_ELEMENT_DATA_TYPE_FLOAT,shape=[-1, 2],dimNames=[batch_size,""])
Tokenizing "I loved this film.":
tokens: [[CLS], i, loved, this, film, ., [SEP]]
input_ids: [101, 1045, 3866, 2023, 2143, 1012, 102]
attention_mask: [1, 1, 1, 1, 1, 1, 1]
Two texts in one batch are padded to the same length:
[[CLS], i, loved, this, film, ., [SEP]]
mask [1, 1, 1, 1, 1, 1, 1]
[[CLS], bad, ., [SEP], [PAD], [PAD], [PAD]]
mask [1, 1, 1, 1, 0, 0, 0]