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Use faster model for examples
Signed-off-by: elronbandel <[email protected]>
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-27
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6 files changed

+62
-27
lines changed

‎examples/evaluate_using_metrics_ensemble.py

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split="test",
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)
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# Infer using Llama-3.2-1B base using HF API
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# Infer using SmolLM2 using HF API
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model = HFPipelineBasedInferenceEngine(
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model_name="Qwen/Qwen1.5-0.5B-Chat", max_new_tokens=32
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model_name="HuggingFaceTB/SmolLM2-135M-Instruct", max_new_tokens=32
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)
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# Change to this to infer with external APIs:
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# CrossProviderInferenceEngine(model="llama-3-2-1b-instruct", provider="watsonx")

‎examples/ner_evaluation.py

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format="formats.chat_api",
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)
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# Infer using Llama-3.2-1B base using HF API
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# Infer using SmolLM2 using HF API
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# model = HFPipelineBasedInferenceEngine(
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# model_name="Qwen/Qwen1.5-0.5B-Chat", max_new_tokens=32
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# model_name="HuggingFaceTB/SmolLM2-135M-Instruct", max_new_tokens=32
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# )
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# Change to this to infer with external APIs:
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‎examples/qa_evaluation.py

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format="formats.chat_api",
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)
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# Infer using Llama-3.2-1B base using HF API
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# Infer using SmolLM2 using HF API
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model = HFPipelineBasedInferenceEngine(
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model_name="Qwen/Qwen1.5-0.5B-Chat", max_new_tokens=32
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model_name="HuggingFaceTB/SmolLM2-135M-Instruct", max_new_tokens=32
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)
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# Change to this to infer with external APIs:
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# from unitxt.inference import CrossProviderInferenceEngine

‎examples/standalone_evaluation_llm_as_judge.py

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max_test_instances=10,
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)
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# Infer using Llama-3.2-1B base using HF API
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# Infer using SmolLM2 using HF API
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model = HFPipelineBasedInferenceEngine(
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model_name="Qwen/Qwen1.5-0.5B-Chat", max_new_tokens=32
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model_name="HuggingFaceTB/SmolLM2-135M-Instruct", max_new_tokens=32
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)
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predictions = model(dataset)
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‎examples/standalone_qa_evaluation.py

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@@ -37,9 +37,9 @@
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)
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# Infer using Llama-3.2-1B base using HF API
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# Infer using SmolLM2 using HF API
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model = HFPipelineBasedInferenceEngine(
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model_name="Qwen/Qwen1.5-0.5B-Chat", max_new_tokens=32
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model_name="HuggingFaceTB/SmolLM2-135M-Instruct", max_new_tokens=32
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)
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# Change to this to infer with external APIs:
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# from unitxt.inference import CrossProviderInferenceEngine

‎utils/.secrets.baseline

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{
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"path": "detect_secrets.filters.allowlist.is_line_allowlisted"
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},
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{
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"path": "detect_secrets.filters.common.is_baseline_file",
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"filename": "utils/.secrets.baseline"
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},
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{
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"path": "detect_secrets.filters.common.is_ignored_due_to_verification_policies",
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"min_level": 2
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}
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],
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"results": {
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"examples/evaluate_using_metrics_ensemble.py": [
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{
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"type": "Base64 High Entropy String",
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"filename": "examples/evaluate_using_metrics_ensemble.py",
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"hashed_secret": "bed3655d44736098fa59a0661d724a73da7c5654",
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"is_verified": false,
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"line_number": 32
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}
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],
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"examples/ner_evaluation.py": [
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{
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"type": "Base64 High Entropy String",
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"filename": "examples/ner_evaluation.py",
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"hashed_secret": "bed3655d44736098fa59a0661d724a73da7c5654",
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"is_verified": false,
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"line_number": 40
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}
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],
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"examples/qa_evaluation.py": [
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{
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"type": "Base64 High Entropy String",
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"filename": "examples/qa_evaluation.py",
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"hashed_secret": "bed3655d44736098fa59a0661d724a73da7c5654",
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"is_verified": false,
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"line_number": 35
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}
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],
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"examples/standalone_evaluation_llm_as_judge.py": [
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{
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"type": "Base64 High Entropy String",
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"filename": "examples/standalone_evaluation_llm_as_judge.py",
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"hashed_secret": "bed3655d44736098fa59a0661d724a73da7c5654",
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"is_verified": false,
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"line_number": 94
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}
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],
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"examples/standalone_qa_evaluation.py": [
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{
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"type": "Base64 High Entropy String",
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"filename": "examples/standalone_qa_evaluation.py",
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"hashed_secret": "bed3655d44736098fa59a0661d724a73da7c5654",
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"is_verified": false,
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"line_number": 42
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}
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],
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"src/unitxt/inference.py": [
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{
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"type": "Secret Keyword",
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"filename": "src/unitxt/inference.py",
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"hashed_secret": "aa6cd2a77de22303be80e1f632195d62d211a729",
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"is_verified": false,
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"line_number": 1294,
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"is_secret": false
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"line_number": 1294
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},
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{
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"type": "Secret Keyword",
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"filename": "src/unitxt/inference.py",
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"hashed_secret": "c8f16a194efc59559549c7bd69f7bea038742e79",
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"is_verified": false,
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"line_number": 1779,
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"is_secret": false
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"line_number": 1779
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}
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],
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"src/unitxt/loaders.py": [
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"filename": "src/unitxt/loaders.py",
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"hashed_secret": "840268f77a57d5553add023cfa8a4d1535f49742",
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"is_verified": false,
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"line_number": 595,
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"is_secret": false
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"line_number": 595
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}
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],
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"src/unitxt/metrics.py": [
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"filename": "src/unitxt/metrics.py",
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"hashed_secret": "fa172616e9af3d2a24b5597f264eab963fe76889",
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"is_verified": false,
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"line_number": 70,
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"is_secret": false
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"line_number": 70
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}
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],
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"tests/library/test_loaders.py": [
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"filename": "tests/library/test_loaders.py",
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"hashed_secret": "8d814baafe5d8412572dc520dcab83f60ce1375c",
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"is_verified": false,
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"line_number": 125,
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"is_secret": false
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"line_number": 125
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},
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{
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"type": "Secret Keyword",
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"filename": "tests/library/test_loaders.py",
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"hashed_secret": "42a472ac88cd8d43a2c5ae0bd0bdf4626cdaba31",
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"is_verified": false,
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"line_number": 135,
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"is_secret": false
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"line_number": 135
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}
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]
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},
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"generated_at": "2025-02-12T09:37:42Z"
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"generated_at": "2025-02-13T13:37:19Z"
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}

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