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 benchmark_predictions.npz from ProCreations/auto-200m-2: direct link, hf CLI and curl.
- Browser
- Download file 72.8 kB
-
https://huggingface.co/ProCreations/auto-200m-2/resolve/main/benchmark_predictions.npz
- Command line
-
hf download hf://ProCreations/auto-200m-2/benchmark_predictions.npz
-
curl -L -o benchmark_predictions.npz https://huggingface.co/ProCreations/auto-200m-2/resolve/main/benchmark_predictions.npz
72.8 kB
- Xet hash:
- d2cba02b46790e4d6d392c0e5fdc5d9738cf57a4499db7b5dfac22040df12fbd
- Size of remote file:
- 72.8 kB
- SHA256:
- 91d6d889fc9c84635569a3e21cb37f68906690d50ae0af18b9740592da7ee919
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