Text Classification
Transformers
ONNX
Safetensors
Rust
English
tool-use
tool-calling
guardrails
final-response-verifier
workflow-verification
shadow-mode
Eval Results (legacy)
Instructions to use cowWhySo/final-response-verifier-classifier-production with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cowWhySo/final-response-verifier-classifier-production with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="cowWhySo/final-response-verifier-classifier-production")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("cowWhySo/final-response-verifier-classifier-production", dtype="auto") - Notebooks
- Google Colab
- Kaggle
Add final-response verifier ONNX artifacts
Browse files- onnx/artifact_manifest.json +1 -1
- onnx/model.onnx +1 -1
- onnx/model_quantized.onnx +1 -1
- onnx/onnx_parity_report.json +3 -3
- onnx/training_provenance.json +21 -21
onnx/artifact_manifest.json
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@@ -49,5 +49,5 @@
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],
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"deployment_default": "shadow",
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"shadow_first_reason": "experimental final-response verifier; promote only after eval replay",
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-
"created_unix":
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}
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],
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"deployment_default": "shadow",
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"shadow_first_reason": "experimental final-response verifier; promote only after eval replay",
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+
"created_unix": 1780095207
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}
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onnx/model.onnx
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@@ -1,3 +1,3 @@
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version https://git-lfs.github.com/spec/v1
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oid sha256:
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size 568055401
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version https://git-lfs.github.com/spec/v1
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+
oid sha256:3849500df5f74f1da797de9bf0c62231639e6d494d2f1ebbbd37b1423dff3adb
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size 568055401
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onnx/model_quantized.onnx
CHANGED
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@@ -1,3 +1,3 @@
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version https://git-lfs.github.com/spec/v1
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oid sha256:
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size 172267901
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version https://git-lfs.github.com/spec/v1
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+
oid sha256:3e6b6ab9ac6c268e164d7bcb04c2bb9d0bd91c4a2b798e7ece0d97a7fc9c01c6
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size 172267901
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onnx/onnx_parity_report.json
CHANGED
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@@ -1,10 +1,10 @@
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{
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"schema_version": "final-response-verifier-onnx-parity/v1",
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-
"rows":
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"pt_fp32_top_label_agreement": 1.0,
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-
"pt_fp32_max_abs_diff":
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"quantized_present": true,
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| 7 |
"fp32_quantized_top_label_agreement": 1.0,
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| 8 |
"fp32_quantized_disagreements": 0,
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| 9 |
-
"fp32_quantized_max_abs_diff": 0.
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}
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{
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"schema_version": "final-response-verifier-onnx-parity/v1",
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+
"rows": 14,
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| 4 |
"pt_fp32_top_label_agreement": 1.0,
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| 5 |
+
"pt_fp32_max_abs_diff": 2.4586915969848633e-07,
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| 6 |
"quantized_present": true,
|
| 7 |
"fp32_quantized_top_label_agreement": 1.0,
|
| 8 |
"fp32_quantized_disagreements": 0,
|
| 9 |
+
"fp32_quantized_max_abs_diff": 0.01770871877670288
|
| 10 |
}
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onnx/training_provenance.json
CHANGED
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@@ -21,38 +21,38 @@
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"force_retrain": false,
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"export_cpu_only": true
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},
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-
"rows":
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-
"train_rows":
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-
"validation_rows":
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-
"test_rows":
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"label_counts": {
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-
"
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-
"
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"unsupported_claim": 18,
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-
"
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| 33 |
"failed_to_acknowledge_data_gap": 18
|
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},
|
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"resumed_from_checkpoint": false,
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"train_metrics": {
|
| 37 |
-
"train_runtime":
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-
"train_samples_per_second":
|
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-
"train_steps_per_second": 0.
|
| 40 |
-
"total_flos":
|
| 41 |
-
"train_loss": 1.
|
| 42 |
"epoch": 3.0
|
| 43 |
},
|
| 44 |
"test_metrics": {
|
| 45 |
-
"eval_loss": 1.
|
| 46 |
-
"eval_accuracy": 0.
|
| 47 |
-
"eval_macro_precision": 0.
|
| 48 |
"eval_macro_recall": 0.2,
|
| 49 |
-
"eval_macro_f1": 0.
|
| 50 |
-
"eval_macro_precision_all_labels": 0.
|
| 51 |
"eval_macro_recall_all_labels": 0.2,
|
| 52 |
-
"eval_macro_f1_all_labels": 0.
|
| 53 |
-
"eval_runtime": 0.
|
| 54 |
-
"eval_samples_per_second":
|
| 55 |
-
"eval_steps_per_second": 2.
|
| 56 |
"epoch": 3.0
|
| 57 |
}
|
| 58 |
}
|
|
|
|
| 21 |
"force_retrain": false,
|
| 22 |
"export_cpu_only": true
|
| 23 |
},
|
| 24 |
+
"rows": 128,
|
| 25 |
+
"train_rows": 97,
|
| 26 |
+
"validation_rows": 17,
|
| 27 |
+
"test_rows": 14,
|
| 28 |
"label_counts": {
|
| 29 |
+
"valid_final_response": 37,
|
| 30 |
+
"contradicts_tool_result": 37,
|
| 31 |
"unsupported_claim": 18,
|
| 32 |
+
"missing_tool_fact": 18,
|
| 33 |
"failed_to_acknowledge_data_gap": 18
|
| 34 |
},
|
| 35 |
"resumed_from_checkpoint": false,
|
| 36 |
"train_metrics": {
|
| 37 |
+
"train_runtime": 10.306,
|
| 38 |
+
"train_samples_per_second": 47.06,
|
| 39 |
+
"train_steps_per_second": 0.97,
|
| 40 |
+
"total_flos": 39847260684000.0,
|
| 41 |
+
"train_loss": 1.6460792223612468,
|
| 42 |
"epoch": 3.0
|
| 43 |
},
|
| 44 |
"test_metrics": {
|
| 45 |
+
"eval_loss": 1.6308313608169556,
|
| 46 |
+
"eval_accuracy": 0.14285714285714285,
|
| 47 |
+
"eval_macro_precision": 0.02857142857142857,
|
| 48 |
"eval_macro_recall": 0.2,
|
| 49 |
+
"eval_macro_f1": 0.05,
|
| 50 |
+
"eval_macro_precision_all_labels": 0.02857142857142857,
|
| 51 |
"eval_macro_recall_all_labels": 0.2,
|
| 52 |
+
"eval_macro_f1_all_labels": 0.05,
|
| 53 |
+
"eval_runtime": 0.4413,
|
| 54 |
+
"eval_samples_per_second": 31.727,
|
| 55 |
+
"eval_steps_per_second": 2.266,
|
| 56 |
"epoch": 3.0
|
| 57 |
}
|
| 58 |
}
|