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examples: add facebook/bart-large-mnli text-classification recipe #933

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examples: add facebook/bart-large-mnli text-classification recipe #933

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1 change: 1 addition & 0 deletions examples/recipes/README.md
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Expand Up @@ -17,6 +17,7 @@ Each *(model, task)* includes:
| Model | Task |
|---|---|
| BAAI/bge-large-en-v1.5 | sentence-similarity |
| facebook/bart-large-mnli | text-classification |
| cardiffnlp/twitter-roberta-base-sentiment-latest | text-classification |
| deepset/roberta-base-squad2 | question-answering |
| deepset/tinyroberta-squad2 | question-answering |
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58 changes: 58 additions & 0 deletions examples/recipes/facebook_bart-large-mnli/text-classification_config.json
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@@ -0,0 +1,58 @@
{
"_note": "Workaround: input_ids.value_range narrowed to [2,3] (eos_token_id=2) so BartForSequenceClassification eos-pooling never sees an empty match during export. Verified end-to-end: build complete (157s), L0 PASS (1042 nodes, opset 17), L1-CPU PASS (1638ms/iter with real input — `winml perf` will fail because random ints lack eos), L2 PASS (cosine=1.0, max_abs=1e-6 vs PyTorch on premise+hypothesis pair). See research/adding-model-support/model_knowledge/bart.json bart-004.",
"export": {
"opset_version": 17,
"batch_size": 1,
"export_params": true,
"do_constant_folding": true,
"verbose": false,
"dynamo": false,
"enable_hierarchy_tags": true,
"clean_onnx": false,
"hierarchy_tag_format": "full",
"input_tensors": [
{
"name": "input_ids",
"dtype": "int32",
"shape": [
1,
1024
],
"value_range": [
2,
3
]
},
{
"name": "attention_mask",
"dtype": "int32",
"shape": [
1,
1024
],
"value_range": [
1,
2
]
}
],
"output_tensors": [
{
"name": "logits"
}
]
},
"optim": {
"clamp_constant_values": true,
"gelu_fusion": true,
"matmul_add_fusion": true,
"remove_isnan_in_attention_mask": true
},
"quant": null,
"compile": null,
"loader": {
"task": "text-classification",
"model_class": "AutoModelForSequenceClassification",
"model_type": "bart"
}
}
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