Text Classification
Transformers
Safetensors
English
modernbert
evalroute
task-routing
gonogo
text-embeddings-inference
Instructions to use keppy/evalroute-lane-encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use keppy/evalroute-lane-encoder with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="keppy/evalroute-lane-encoder")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("keppy/evalroute-lane-encoder") model = AutoModelForSequenceClassification.from_pretrained("keppy/evalroute-lane-encoder", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download eval_predictions.jsonl from keppy/evalroute-lane-encoder: direct link, hf CLI and curl.
- Browser
- Download file 2.64 kB
-
https://huggingface.co/keppy/evalroute-lane-encoder/resolve/main/eval_predictions.jsonl
- Command line
-
hf download hf://keppy/evalroute-lane-encoder/eval_predictions.jsonl
-
curl -L -o eval_predictions.jsonl https://huggingface.co/keppy/evalroute-lane-encoder/resolve/main/eval_predictions.jsonl
2.64 kB
| {"id": "6292a60a9a2241e08603b294ae393b3c", "label": "routine-coding", "predicted": "long-doc-reading", "confidence": 0.41951555013656616} | |
| {"id": "72c3a76e6eb04afdba944b8cbd8bf9b0", "label": "routine-coding", "predicted": "long-doc-reading", "confidence": 0.6065808534622192} | |
| {"id": "a9f795fd0bb5419fb634cd98cb9b07b2", "label": "routine-coding", "predicted": "routine-coding", "confidence": 0.7151268124580383} | |
| {"id": "fa85d94d4b0f4f59bb3d334dcc27004d", "label": "dl-ml-research-engineering", "predicted": "routine-coding", "confidence": 0.49763408303260803} | |
| {"id": "1a84b311446b4571969af0f31c7ffd70", "label": "routine-coding", "predicted": "web-research", "confidence": 0.49626997113227844} | |
| {"id": "e025c31944f446de9e69bb969adad8b1", "label": "routine-coding", "predicted": "prose", "confidence": 0.32139402627944946} | |
| {"id": "ece1863753294f4bbbaa36527cc662a5", "label": "routine-coding", "predicted": "prose", "confidence": 0.5279013514518738} | |
| {"id": "e47fca02cf0542eab1b25ef8398b80c5", "label": "routine-coding", "predicted": "hard-agentic-coding", "confidence": 0.800709068775177} | |
| {"id": "540704307436479487184f6b019ee4bd", "label": "routine-coding", "predicted": "routine-coding", "confidence": 0.8717922568321228} | |
| {"id": "taskset:align-interpretability-circuits", "label": "alignment-reasoning", "predicted": "alignment-reasoning", "confidence": 0.953163743019104} | |
| {"id": "taskset:align-shard-theory", "label": "alignment-reasoning", "predicted": "alignment-reasoning", "confidence": 0.866253674030304} | |
| {"id": "taskset:align-emergent-deception", "label": "alignment-reasoning", "predicted": "alignment-reasoning", "confidence": 0.5977520942687988} | |
| {"id": "taskset:dl-grad-accum", "label": "dl-ml-research-engineering", "predicted": "dl-ml-research-engineering", "confidence": 0.9232227802276611} | |
| {"id": "taskset:dl-train-loop-epochs", "label": "dl-ml-research-engineering", "predicted": "dl-ml-research-engineering", "confidence": 0.7878546118736267} | |
| {"id": "taskset:dl-early-stop-patience", "label": "dl-ml-research-engineering", "predicted": "dl-ml-research-engineering", "confidence": 0.9592454433441162} | |
| {"id": "taskset:rc-log-parse", "label": "routine-coding", "predicted": "routine-coding", "confidence": 0.8627690672874451} | |
| {"id": "taskset:rc-csv-group-count", "label": "routine-coding", "predicted": "routine-coding", "confidence": 0.7975051999092102} | |
| {"id": "taskset:rc-backoff-schedule", "label": "routine-coding", "predicted": "routine-coding", "confidence": 0.47848784923553467} | |
| {"id": "taskset:rc-iso-dates", "label": "routine-coding", "predicted": "routine-coding", "confidence": 0.7108357548713684} | |