Image-Text-to-Text
Transformers
Safetensors
English
Chinese
qwen3_5
qwen3.5
decision-model
structured-prediction
text-classification
conversational
Instructions to use PelaAI/KnowLine-9B-Gen1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use PelaAI/KnowLine-9B-Gen1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="PelaAI/KnowLine-9B-Gen1") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] pipe(text=messages)# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("PelaAI/KnowLine-9B-Gen1") model = AutoModelForMultimodalLM.from_pretrained("PelaAI/KnowLine-9B-Gen1", device_map="auto") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] inputs = processor.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=256) print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use PelaAI/KnowLine-9B-Gen1 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "PelaAI/KnowLine-9B-Gen1" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "PelaAI/KnowLine-9B-Gen1", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker
docker model run hf.co/PelaAI/KnowLine-9B-Gen1
- SGLang
How to use PelaAI/KnowLine-9B-Gen1 with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "PelaAI/KnowLine-9B-Gen1" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "PelaAI/KnowLine-9B-Gen1", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "PelaAI/KnowLine-9B-Gen1" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "PelaAI/KnowLine-9B-Gen1", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }' - Docker Model Runner
How to use PelaAI/KnowLine-9B-Gen1 with Docker Model Runner:
docker model run hf.co/PelaAI/KnowLine-9B-Gen1
Document retained mixU source-family exposure for S1MB
Browse files- SHA256SUMS +1 -0
- TRAINING_SOURCES.json +1622 -0
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| 1 |
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| 2 |
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|
| 3 |
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| 4 |
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| 6 |
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| 7 |
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| 8 |
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| 9 |
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| 10 |
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| 11 |
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| 12 |
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|
| 13 |
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|
| 14 |
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|
| 15 |
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|
| 16 |
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| 17 |
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| 18 |
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| 19 |
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| 20 |
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| 21 |
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| 22 |
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| 23 |
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| 24 |
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| 25 |
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| 26 |
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| 27 |
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| 28 |
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| 29 |
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| 30 |
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| 31 |
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| 32 |
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| 33 |
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| 34 |
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| 35 |
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| 36 |
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| 37 |
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| 38 |
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| 39 |
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| 40 |
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| 41 |
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| 42 |
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| 43 |
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| 44 |
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| 45 |
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| 46 |
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| 47 |
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| 48 |
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| 49 |
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| 50 |
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| 51 |
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| 52 |
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| 53 |
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| 54 |
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| 55 |
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| 56 |
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| 57 |
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| 58 |
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| 62 |
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| 63 |
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| 64 |
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| 65 |
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| 66 |
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| 174 |
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| 175 |
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| 176 |
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| 177 |
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| 178 |
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| 179 |
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| 180 |
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| 181 |
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| 182 |
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| 183 |
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| 184 |
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| 185 |
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| 186 |
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| 187 |
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| 188 |
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| 189 |
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| 190 |
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| 191 |
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| 192 |
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| 193 |
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| 194 |
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| 195 |
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| 196 |
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| 197 |
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| 203 |
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| 204 |
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| 205 |
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| 210 |
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| 214 |
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| 233 |
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|
| 234 |
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| 235 |
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| 236 |
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| 237 |
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| 240 |
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| 242 |
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| 243 |
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| 244 |
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| 245 |
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| 246 |
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| 247 |
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|
| 248 |
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| 249 |
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| 250 |
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| 251 |
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| 252 |
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| 253 |
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| 254 |
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| 255 |
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| 256 |
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| 257 |
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| 258 |
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| 259 |
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| 260 |
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| 261 |
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|
| 262 |
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|
| 263 |
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|
| 264 |
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|
| 265 |
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|
| 266 |
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|
| 267 |
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|
| 268 |
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|
| 269 |
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|
| 270 |
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|
| 271 |
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|
| 272 |
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|
| 273 |
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|
| 274 |
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|
| 275 |
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| 276 |
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|
| 277 |
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|
| 278 |
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|
| 279 |
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| 280 |
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|
| 281 |
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|
| 282 |
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| 283 |
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|
| 284 |
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|
| 285 |
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|
| 286 |
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| 287 |
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| 289 |
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| 290 |
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| 291 |
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| 292 |
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|
| 293 |
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|
| 294 |
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|
| 295 |
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|
| 296 |
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|
| 297 |
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| 298 |
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| 840 |
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| 841 |
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| 849 |
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| 850 |
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| 861 |
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| 862 |
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| 863 |
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| 864 |
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| 865 |
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| 873 |
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| 874 |
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| 876 |
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| 877 |
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| 878 |
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| 879 |
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| 884 |
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| 886 |
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| 888 |
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| 891 |
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| 892 |
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| 893 |
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| 894 |
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| 895 |
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| 896 |
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| 897 |
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| 899 |
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| 900 |
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| 902 |
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| 903 |
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| 904 |
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| 905 |
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| 906 |
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| 907 |
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| 909 |
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| 910 |
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| 911 |
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| 912 |
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| 913 |
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| 914 |
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| 915 |
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| 916 |
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| 917 |
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| 922 |
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| 924 |
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| 925 |
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| 927 |
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| 928 |
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| 929 |
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| 930 |
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| 931 |
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| 932 |
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| 933 |
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| 934 |
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| 935 |
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| 936 |
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| 937 |
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| 938 |
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| 939 |
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| 940 |
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| 941 |
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| 942 |
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| 943 |
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| 944 |
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| 945 |
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| 1280 |
+
"relationship": "same_source_family",
|
| 1281 |
+
"source_description": "Open-Jev 1.1 training data (the Open-Jev control families).",
|
| 1282 |
+
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|
| 1283 |
+
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|
| 1284 |
+
"oj11_community": 1944
|
| 1285 |
+
},
|
| 1286 |
+
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|
| 1287 |
+
"support_count_scope": "Rows in supporting mixed channels; not necessarily all from this upstream source",
|
| 1288 |
+
"split": "train"
|
| 1289 |
+
},
|
| 1290 |
+
{
|
| 1291 |
+
"benchmark_id": "open-jev--snake-v1-choice-test-v1",
|
| 1292 |
+
"relationship": "same_source_family",
|
| 1293 |
+
"source_description": "Open-Jev 1.1 training data (the Open-Jev control families).",
|
| 1294 |
+
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|
| 1295 |
+
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|
| 1296 |
+
"oj11_community": 1944
|
| 1297 |
+
},
|
| 1298 |
+
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|
| 1299 |
+
"support_count_scope": "Rows in supporting mixed channels; not necessarily all from this upstream source",
|
| 1300 |
+
"split": "train"
|
| 1301 |
+
},
|
| 1302 |
+
{
|
| 1303 |
+
"benchmark_id": "open-jev--snake-v1-noul-test-v1",
|
| 1304 |
+
"relationship": "same_source_family",
|
| 1305 |
+
"source_description": "Open-Jev 1.1 training data (the Open-Jev control families).",
|
| 1306 |
+
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|
| 1307 |
+
"oj11_replay": 2187,
|
| 1308 |
+
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|
| 1309 |
+
},
|
| 1310 |
+
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|
| 1311 |
+
"support_count_scope": "Rows in supporting mixed channels; not necessarily all from this upstream source",
|
| 1312 |
+
"split": "train"
|
| 1313 |
+
},
|
| 1314 |
+
{
|
| 1315 |
+
"benchmark_id": "open-jev--sponsor-segment-control-v1-choice-test-v1",
|
| 1316 |
+
"relationship": "same_source_family",
|
| 1317 |
+
"source_description": "Open-Jev 1.1 training data (the Open-Jev control families).",
|
| 1318 |
+
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|
| 1319 |
+
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|
| 1320 |
+
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|
| 1321 |
+
},
|
| 1322 |
+
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|
| 1323 |
+
"support_count_scope": "Rows in supporting mixed channels; not necessarily all from this upstream source",
|
| 1324 |
+
"split": "train"
|
| 1325 |
+
},
|
| 1326 |
+
{
|
| 1327 |
+
"benchmark_id": "open-jev--tic-tac-toe-v1-choice-test-v1",
|
| 1328 |
+
"relationship": "same_source_family",
|
| 1329 |
+
"source_description": "Open-Jev 1.1 training data (the Open-Jev control families).",
|
| 1330 |
+
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|
| 1331 |
+
"oj11_replay": 2187,
|
| 1332 |
+
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|
| 1333 |
+
},
|
| 1334 |
+
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|
| 1335 |
+
"support_count_scope": "Rows in supporting mixed channels; not necessarily all from this upstream source",
|
| 1336 |
+
"split": "train"
|
| 1337 |
+
},
|
| 1338 |
+
{
|
| 1339 |
+
"benchmark_id": "open-jev--vizdoom-basic-v1-noul-test-v1",
|
| 1340 |
+
"relationship": "same_source_family",
|
| 1341 |
+
"source_description": "Open-Jev 1.1 training data (the Open-Jev control families).",
|
| 1342 |
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|
| 1343 |
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"oj11_replay": 2187,
|
| 1344 |
+
"oj11_community": 1944
|
| 1345 |
+
},
|
| 1346 |
+
"support_channel_rows": 4131,
|
| 1347 |
+
"support_count_scope": "Rows in supporting mixed channels; not necessarily all from this upstream source",
|
| 1348 |
+
"split": "train"
|
| 1349 |
+
},
|
| 1350 |
+
{
|
| 1351 |
+
"benchmark_id": "open-jev--vizdoom-basic-v1-score-test-v1",
|
| 1352 |
+
"relationship": "same_source_family",
|
| 1353 |
+
"source_description": "Open-Jev 1.1 training data (the Open-Jev control families).",
|
| 1354 |
+
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|
| 1355 |
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"oj11_replay": 2187,
|
| 1356 |
+
"oj11_community": 1944
|
| 1357 |
+
},
|
| 1358 |
+
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|
| 1359 |
+
"support_count_scope": "Rows in supporting mixed channels; not necessarily all from this upstream source",
|
| 1360 |
+
"split": "train"
|
| 1361 |
+
},
|
| 1362 |
+
{
|
| 1363 |
+
"benchmark_id": "open-jev--workflow-controls-v1--agent-trace-observability-noul-test-v1",
|
| 1364 |
+
"relationship": "same_source_family",
|
| 1365 |
+
"source_description": "Open-Jev 1.1 training data (the Open-Jev control families).",
|
| 1366 |
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|
| 1367 |
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"oj11_replay": 2187,
|
| 1368 |
+
"oj11_community": 1944
|
| 1369 |
+
},
|
| 1370 |
+
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|
| 1371 |
+
"support_count_scope": "Rows in supporting mixed channels; not necessarily all from this upstream source",
|
| 1372 |
+
"split": "train"
|
| 1373 |
+
},
|
| 1374 |
+
{
|
| 1375 |
+
"benchmark_id": "open-jev--workflow-controls-v1--customer-service-noul-test-v1",
|
| 1376 |
+
"relationship": "same_source_family",
|
| 1377 |
+
"source_description": "Open-Jev 1.1 training data (the Open-Jev control families).",
|
| 1378 |
+
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|
| 1379 |
+
"oj11_replay": 2187,
|
| 1380 |
+
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|
| 1381 |
+
},
|
| 1382 |
+
"support_channel_rows": 4131,
|
| 1383 |
+
"support_count_scope": "Rows in supporting mixed channels; not necessarily all from this upstream source",
|
| 1384 |
+
"split": "train"
|
| 1385 |
+
},
|
| 1386 |
+
{
|
| 1387 |
+
"benchmark_id": "open-jev--workflow-controls-v1--invoice-processing-noul-test-v1",
|
| 1388 |
+
"relationship": "same_source_family",
|
| 1389 |
+
"source_description": "Open-Jev 1.1 training data (the Open-Jev control families).",
|
| 1390 |
+
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|
| 1391 |
+
"oj11_replay": 2187,
|
| 1392 |
+
"oj11_community": 1944
|
| 1393 |
+
},
|
| 1394 |
+
"support_channel_rows": 4131,
|
| 1395 |
+
"support_count_scope": "Rows in supporting mixed channels; not necessarily all from this upstream source",
|
| 1396 |
+
"split": "train"
|
| 1397 |
+
},
|
| 1398 |
+
{
|
| 1399 |
+
"benchmark_id": "open-jev--workflow-controls-v1--security-incidents-noul-test-v1",
|
| 1400 |
+
"relationship": "same_source_family",
|
| 1401 |
+
"source_description": "Open-Jev 1.1 training data (the Open-Jev control families).",
|
| 1402 |
+
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|
| 1403 |
+
"oj11_replay": 2187,
|
| 1404 |
+
"oj11_community": 1944
|
| 1405 |
+
},
|
| 1406 |
+
"support_channel_rows": 4131,
|
| 1407 |
+
"support_count_scope": "Rows in supporting mixed channels; not necessarily all from this upstream source",
|
| 1408 |
+
"split": "train"
|
| 1409 |
+
},
|
| 1410 |
+
{
|
| 1411 |
+
"benchmark_id": "openbookqa-choice-test-v1",
|
| 1412 |
+
"relationship": "same_source_family",
|
| 1413 |
+
"source_description": "OpenBookQA (allenai/openbookqa) train split.",
|
| 1414 |
+
"retained_support_channels": {
|
| 1415 |
+
"hf_di_openbookqa": 801,
|
| 1416 |
+
"hf_di_openbookqa_fmt": 403
|
| 1417 |
+
},
|
| 1418 |
+
"support_channel_rows": 1204,
|
| 1419 |
+
"support_count_scope": "Rows in supporting mixed channels; not necessarily all from this upstream source",
|
| 1420 |
+
"split": "train"
|
| 1421 |
+
},
|
| 1422 |
+
{
|
| 1423 |
+
"benchmark_id": "ruletaker-noul-test-v1",
|
| 1424 |
+
"relationship": "same_source_family",
|
| 1425 |
+
"source_description": "RuleTaker / ProofWriter (tasksource/ruletaker, tasksource/proofwriter) train splits.",
|
| 1426 |
+
"retained_support_channels": {
|
| 1427 |
+
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|
| 1428 |
+
"kd_kn_ruletaker": 415,
|
| 1429 |
+
"hf_kn_proofwriter": 593,
|
| 1430 |
+
"hf_kn_ruletaker": 276
|
| 1431 |
+
},
|
| 1432 |
+
"support_channel_rows": 2096,
|
| 1433 |
+
"support_count_scope": "Rows in supporting mixed channels; not necessarily all from this upstream source",
|
| 1434 |
+
"split": "train"
|
| 1435 |
+
},
|
| 1436 |
+
{
|
| 1437 |
+
"benchmark_id": "sgd-choice-test-v1",
|
| 1438 |
+
"relationship": "same_source_family",
|
| 1439 |
+
"source_description": "Schema-Guided Dialogue (dstc8-schema-guided-dialogue) train split.",
|
| 1440 |
+
"retained_support_channels": {
|
| 1441 |
+
"kd_di_sgd": 961,
|
| 1442 |
+
"h_sgd": 1290,
|
| 1443 |
+
"h_sgd_fmt": 376,
|
| 1444 |
+
"inj2_h_sgd": 281
|
| 1445 |
+
},
|
| 1446 |
+
"support_channel_rows": 2908,
|
| 1447 |
+
"support_count_scope": "Rows in supporting mixed channels; not necessarily all from this upstream source",
|
| 1448 |
+
"split": "train"
|
| 1449 |
+
},
|
| 1450 |
+
{
|
| 1451 |
+
"benchmark_id": "snli-choice-test-v1",
|
| 1452 |
+
"relationship": "same_source_family",
|
| 1453 |
+
"source_description": "SNLI (stanfordnlp/snli) train split.",
|
| 1454 |
+
"retained_support_channels": {
|
| 1455 |
+
"kd_gen_snli": 1267,
|
| 1456 |
+
"hf_gen_snli": 750
|
| 1457 |
+
},
|
| 1458 |
+
"support_channel_rows": 2017,
|
| 1459 |
+
"support_count_scope": "Rows in supporting mixed channels; not necessarily all from this upstream source",
|
| 1460 |
+
"split": "train"
|
| 1461 |
+
},
|
| 1462 |
+
{
|
| 1463 |
+
"benchmark_id": "argument-quality-choice-test-v1",
|
| 1464 |
+
"relationship": "same_source_family",
|
| 1465 |
+
"source_description": "IBM-ArgQ-Rank-30k (ibm-research/argument_quality_ranking_30k) train split.",
|
| 1466 |
+
"retained_support_channels": {
|
| 1467 |
+
"kd_mj_argq_stance": 610,
|
| 1468 |
+
"hf_mj_argq_stance": 459,
|
| 1469 |
+
"hf_mj_argq_pair": 424
|
| 1470 |
+
},
|
| 1471 |
+
"support_channel_rows": 1493,
|
| 1472 |
+
"support_count_scope": "Rows in supporting mixed channels; not necessarily all from this upstream source",
|
| 1473 |
+
"split": "train"
|
| 1474 |
+
},
|
| 1475 |
+
{
|
| 1476 |
+
"benchmark_id": "argument-quality-noul-test-v1",
|
| 1477 |
+
"relationship": "same_source_family",
|
| 1478 |
+
"source_description": "IBM-ArgQ-Rank-30k (ibm-research/argument_quality_ranking_30k) train split.",
|
| 1479 |
+
"retained_support_channels": {
|
| 1480 |
+
"kd_mj_argq_stance": 610,
|
| 1481 |
+
"hf_mj_argq_stance": 459,
|
| 1482 |
+
"hf_mj_argq_pair": 424
|
| 1483 |
+
},
|
| 1484 |
+
"support_channel_rows": 1493,
|
| 1485 |
+
"support_count_scope": "Rows in supporting mixed channels; not necessarily all from this upstream source",
|
| 1486 |
+
"split": "train"
|
| 1487 |
+
},
|
| 1488 |
+
{
|
| 1489 |
+
"benchmark_id": "scicite-choice-test-v1",
|
| 1490 |
+
"relationship": "same_source_family",
|
| 1491 |
+
"source_description": "SciCite (allenai/scicite) train split.",
|
| 1492 |
+
"retained_support_channels": {
|
| 1493 |
+
"kd_mj_scicite": 612,
|
| 1494 |
+
"hf_mj_scicite": 417
|
| 1495 |
+
},
|
| 1496 |
+
"support_channel_rows": 1029,
|
| 1497 |
+
"support_count_scope": "Rows in supporting mixed channels; not necessarily all from this upstream source",
|
| 1498 |
+
"split": "train"
|
| 1499 |
+
},
|
| 1500 |
+
{
|
| 1501 |
+
"benchmark_id": "scitail-noul-test-v1",
|
| 1502 |
+
"relationship": "same_source_family",
|
| 1503 |
+
"source_description": "SciTail (allenai/scitail, snli_format) train split.",
|
| 1504 |
+
"retained_support_channels": {
|
| 1505 |
+
"kd_mj_scitail": 624,
|
| 1506 |
+
"hf_mj_scitail": 443,
|
| 1507 |
+
"inj3_hf_mj_scitail": 150
|
| 1508 |
+
},
|
| 1509 |
+
"support_channel_rows": 1217,
|
| 1510 |
+
"support_count_scope": "Rows in supporting mixed channels; not necessarily all from this upstream source",
|
| 1511 |
+
"split": "train"
|
| 1512 |
+
},
|
| 1513 |
+
{
|
| 1514 |
+
"benchmark_id": "patent-similarity-score-test-v1",
|
| 1515 |
+
"relationship": "same_source_family",
|
| 1516 |
+
"source_description": "Google patent phrase similarity train.csv (the source of tasksource/patent-phrase-similarity).",
|
| 1517 |
+
"retained_support_channels": {
|
| 1518 |
+
"kd_patent": 587,
|
| 1519 |
+
"hf_patent": 366
|
| 1520 |
+
},
|
| 1521 |
+
"support_channel_rows": 953,
|
| 1522 |
+
"support_count_scope": "Rows in supporting mixed channels; not necessarily all from this upstream source",
|
| 1523 |
+
"split": "train"
|
| 1524 |
+
},
|
| 1525 |
+
{
|
| 1526 |
+
"benchmark_id": "aegis2-noul-test-v1",
|
| 1527 |
+
"relationship": "same_source_family",
|
| 1528 |
+
"source_description": "Aegis 2.0 safety/routing sources inherited through or1d. The earlier source declaration covered train and validation; retained per-split counts have not been reconstructed for mixU.",
|
| 1529 |
+
"retained_support_channels": {
|
| 1530 |
+
"cand_router_hazard": 5500,
|
| 1531 |
+
"cand_router_safety": 5500,
|
| 1532 |
+
"kd_gen_aegis_prompt": 826
|
| 1533 |
+
},
|
| 1534 |
+
"support_channel_rows": 11826,
|
| 1535 |
+
"support_count_scope": "Rows in supporting mixed channels; not necessarily all from this upstream source",
|
| 1536 |
+
"split": null
|
| 1537 |
+
},
|
| 1538 |
+
{
|
| 1539 |
+
"benchmark_id": "laya--enron-spam-noul-test-v1",
|
| 1540 |
+
"relationship": "same_source_family",
|
| 1541 |
+
"source_description": "Enron-Spam (SetFit/enron_spam) train split.",
|
| 1542 |
+
"retained_support_channels": {
|
| 1543 |
+
"kd_di_enron_spam": 556
|
| 1544 |
+
},
|
| 1545 |
+
"support_channel_rows": 556,
|
| 1546 |
+
"support_count_scope": "Rows in supporting mixed channels; not necessarily all from this upstream source",
|
| 1547 |
+
"split": "train"
|
| 1548 |
+
},
|
| 1549 |
+
{
|
| 1550 |
+
"benchmark_id": "hh-rlhf-choice-test-v1",
|
| 1551 |
+
"relationship": "same_source_family",
|
| 1552 |
+
"source_description": "BeaverTails (PKU-Alignment/BeaverTails 30k_train) and Aegis 2.0 train-split prompts, which reuse Anthropic HH-RLHF red-team prompts.",
|
| 1553 |
+
"retained_support_channels": {
|
| 1554 |
+
"kd_gen_aegis_prompt": 826,
|
| 1555 |
+
"kd_g_beavertails": 761,
|
| 1556 |
+
"hf_g_beavertails": 803
|
| 1557 |
+
},
|
| 1558 |
+
"support_channel_rows": 2390,
|
| 1559 |
+
"support_count_scope": "Rows in supporting mixed channels; not necessarily all from this upstream source",
|
| 1560 |
+
"split": "train"
|
| 1561 |
+
},
|
| 1562 |
+
{
|
| 1563 |
+
"benchmark_id": "synthetic-relevance--nanocoir-noul-test-v1",
|
| 1564 |
+
"relationship": "related_domain",
|
| 1565 |
+
"source_description": "DeepMind CodeContests training problems; related code-retrieval domain only, not established CoIR source identity.",
|
| 1566 |
+
"retained_support_channels": {
|
| 1567 |
+
"kd_code_contests": 1620,
|
| 1568 |
+
"hf_code_contests": 616
|
| 1569 |
+
},
|
| 1570 |
+
"support_channel_rows": 2236,
|
| 1571 |
+
"support_count_scope": "Rows in supporting mixed channels; not necessarily all from this upstream source",
|
| 1572 |
+
"split": null
|
| 1573 |
+
},
|
| 1574 |
+
{
|
| 1575 |
+
"benchmark_id": "synthetic-relevance--nanocoir-score-test-v1",
|
| 1576 |
+
"relationship": "related_domain",
|
| 1577 |
+
"source_description": "DeepMind CodeContests training problems; related code-retrieval domain only, not established CoIR source identity.",
|
| 1578 |
+
"retained_support_channels": {
|
| 1579 |
+
"kd_code_contests": 1620,
|
| 1580 |
+
"hf_code_contests": 616
|
| 1581 |
+
},
|
| 1582 |
+
"support_channel_rows": 2236,
|
| 1583 |
+
"support_count_scope": "Rows in supporting mixed channels; not necessarily all from this upstream source",
|
| 1584 |
+
"split": null
|
| 1585 |
+
},
|
| 1586 |
+
{
|
| 1587 |
+
"benchmark_id": "few-nerd-choice-test-v1",
|
| 1588 |
+
"relationship": "same_source_family",
|
| 1589 |
+
"source_description": "Few-NERD supervised source sentences recast into PII-redaction routing in the inherited or1d component; exact retained upstream split membership not re-audited.",
|
| 1590 |
+
"retained_support_channels": {
|
| 1591 |
+
"cand_router_pii": 5499
|
| 1592 |
+
},
|
| 1593 |
+
"support_channel_rows": 5499,
|
| 1594 |
+
"support_count_scope": "Rows in supporting mixed channels; not necessarily all from this upstream source",
|
| 1595 |
+
"split": null
|
| 1596 |
+
},
|
| 1597 |
+
{
|
| 1598 |
+
"benchmark_id": "gretel-pii-choice-test-v1",
|
| 1599 |
+
"relationship": "related_domain",
|
| 1600 |
+
"source_description": "yes/no PII-redaction questions built from Gretel's synthetic financial documents (gretelai/synthetic_pii_finance_multilingual), a different Gretel dataset from the gretelai/gretel-pii-masking-en-v1 data behind this benchmark; shared source identity is not claimed.",
|
| 1601 |
+
"retained_support_channels": {
|
| 1602 |
+
"cand_router_pii": 5499
|
| 1603 |
+
},
|
| 1604 |
+
"support_channel_rows": 5499,
|
| 1605 |
+
"support_count_scope": "Rows in supporting mixed channels; not necessarily all from this upstream source",
|
| 1606 |
+
"split": null
|
| 1607 |
+
},
|
| 1608 |
+
{
|
| 1609 |
+
"benchmark_id": "massive-choice-test-v1",
|
| 1610 |
+
"relationship": "same_source_family",
|
| 1611 |
+
"source_description": "OpenJevData-140k intent-routing source family; its source notices list MASSIVE. Exact MASSIVE split/locales and retained item identities are unknown.",
|
| 1612 |
+
"retained_support_channels": {
|
| 1613 |
+
"openjevdata_intent_routing": 2242,
|
| 1614 |
+
"inj2_openjevdata_intent_routing": 92,
|
| 1615 |
+
"inj3_openjevdata_intent_routing": 69
|
| 1616 |
+
},
|
| 1617 |
+
"support_channel_rows": 2403,
|
| 1618 |
+
"support_count_scope": "Rows in supporting mixed channels; not necessarily all from this upstream source",
|
| 1619 |
+
"split": null
|
| 1620 |
+
}
|
| 1621 |
+
]
|
| 1622 |
+
}
|