Text Classification
Transformers
Safetensors
modernbert
agent-safety
tool-calling
long-context
distillation
Eval Results (legacy)
text-embeddings-inference
Instructions to use ProCreations/auto-200m-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ProCreations/auto-200m-2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ProCreations/auto-200m-2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ProCreations/auto-200m-2") model = AutoModelForSequenceClassification.from_pretrained("ProCreations/auto-200m-2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download eval/rope_sweep.json from ProCreations/auto-200m-2: direct link, hf CLI and curl.
- Browser
- Download file 1.31 kB
-
https://huggingface.co/ProCreations/auto-200m-2/resolve/main/eval/rope_sweep.json
- Command line
-
hf download hf://ProCreations/auto-200m-2/eval/rope_sweep.json
-
curl -L -o rope_sweep.json https://huggingface.co/ProCreations/auto-200m-2/resolve/main/eval/rope_sweep.json
1.31 kB
| { | |
| "results": [ | |
| { | |
| "theta": 160000.0, | |
| "short": 0.9160820834562606, | |
| "mid": 1.0387379115148965, | |
| "long": 1.1367697905727685, | |
| "seconds": 26.022907972335815 | |
| }, | |
| { | |
| "theta": 640000.0, | |
| "short": 0.9236352910098977, | |
| "mid": 1.0243582937320355, | |
| "long": 0.9822827437326973, | |
| "seconds": 11.011847972869873 | |
| }, | |
| { | |
| "theta": 1280000.0, | |
| "short": 0.9304989230334977, | |
| "mid": 1.031460880067349, | |
| "long": 0.9601743056856983, | |
| "seconds": 11.087600231170654 | |
| }, | |
| { | |
| "theta": 2560000.0, | |
| "short": 0.939639166783192, | |
| "mid": 1.044918367936274, | |
| "long": 0.9650228895573634, | |
| "seconds": 11.14451551437378 | |
| }, | |
| { | |
| "theta": 5120000.0, | |
| "short": 0.9498286157172525, | |
| "mid": 1.0788166451562344, | |
| "long": 1.0147006089168509, | |
| "seconds": 11.193717956542969 | |
| }, | |
| { | |
| "theta": 10240000.0, | |
| "short": 0.9709671184858385, | |
| "mid": 1.0868880858439343, | |
| "long": 0.9965657126456587, | |
| "seconds": 11.231298685073853 | |
| } | |
| ], | |
| "chosen_theta": 1280000.0, | |
| "samples": { | |
| "short": 256, | |
| "mid": 96, | |
| "long": 64 | |
| }, | |
| "rule": "min(mid + long masked-LM loss) with short-context loss within 5% of the original 160k theta; 15% masking; training text only" | |
| } | |