Datasets:
Tasks:
Text Generation
Modalities:
Text
Formats:
parquet
Languages:
English
Size:
10K - 100K
License:
Card: add published retrieval+validator numbers and Intent finder link
Browse files
README.md
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@@ -75,7 +75,7 @@ MIT, matching the repo. CLI command names and syntax are functional facts belong
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## Derived
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- [gesh75/netops-intent2cli](https://huggingface.co/datasets/gesh75/netops-intent2cli): synthetic operator intents paired with verbatim commands from this corpus. Default config is v1 (565 train / 102 validation / 108 test). Config `v2` adds 650 training pairs and an 88-row `operator_test`.
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- [gesh75/netops-intent2cli-qwen0.5b-lora](https://huggingface.co/gesh75/netops-intent2cli-qwen0.5b-lora): experimental Qwen2.5-0.5B LoRA trained on v2.
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## Load
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| 📡 **[netlog-ai](https://huggingface.co/spaces/gesh75/netlog-ai)** | Sanitize-first network log analysis — browser lite demo of [netlog-ai](https://pypi.org/project/netlog-ai/) (PyPI 2.1.0) |
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| 🛠️ **[multivendor-cli-configurator](https://huggingface.co/spaces/gesh75/multivendor-cli-configurator)** | 70k+ CLI commands across 17 vendors — cheatsheet, N-vendor compare, CLI Studio |
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| 🗂️ **[multivendor-cli-commands](https://huggingface.co/datasets/gesh75/multivendor-cli-commands)** | The same 70,006 commands as a Parquet dataset |
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| 🧭 **[netops-intent2cli](https://huggingface.co/datasets/gesh75/netops-intent2cli)** | v1 + v2 synthetic operator intents → corpus command pairs |
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| 🤖 **[netops-intent2cli-qwen0.5b-lora](https://huggingface.co/gesh75/netops-intent2cli-qwen0.5b-lora)** | LoRA adapter: English intent → vendor CLI (experimental) |
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## Derived
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- [gesh75/netops-intent2cli](https://huggingface.co/datasets/gesh75/netops-intent2cli): synthetic operator intents paired with verbatim commands from this corpus. Default config is v1 (565 train / 102 validation / 108 test). Config `v2` adds 650 training pairs and an 88-row `operator_test`.
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- [gesh75/netops-intent2cli-qwen0.5b-lora](https://huggingface.co/gesh75/netops-intent2cli-qwen0.5b-lora): experimental Qwen2.5-0.5B LoRA trained on v2. Adapter-only exact match: **12/108 (11.1%)** on the frozen v1 test, **9/88 (10.2%)** on `operator_test`. Optional retrieval+validator lookup mode (same card): **63/108** and **58/88** when the gold command is already in this corpus; it cannot return a command that is missing from the base. Not production-safe. Browser demo: [Intent finder](https://gesh75-multivendor-cli-configurator.static.hf.space/intent-finder.html) (retrieval only, no model).
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## Load
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|---|---|
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| 📡 **[netlog-ai](https://huggingface.co/spaces/gesh75/netlog-ai)** | Sanitize-first network log analysis — browser lite demo of [netlog-ai](https://pypi.org/project/netlog-ai/) (PyPI 2.1.0) |
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| 🛠️ **[multivendor-cli-configurator](https://huggingface.co/spaces/gesh75/multivendor-cli-configurator)** | 70k+ CLI commands across 17 vendors — cheatsheet, N-vendor compare, CLI Studio, Intent finder |
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| 🗂️ **[multivendor-cli-commands](https://huggingface.co/datasets/gesh75/multivendor-cli-commands)** | The same 70,006 commands as a Parquet dataset |
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| 🧭 **[netops-intent2cli](https://huggingface.co/datasets/gesh75/netops-intent2cli)** | v1 + v2 synthetic operator intents → corpus command pairs |
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| 🤖 **[netops-intent2cli-qwen0.5b-lora](https://huggingface.co/gesh75/netops-intent2cli-qwen0.5b-lora)** | LoRA adapter: English intent → vendor CLI (experimental) |
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