Text Classification
Transformers
Safetensors
jev-style
English
modernbert
feature-extraction
decision-model
typed-decisions
system-one
decision-index
calibration
ettin
cross-encoder
zero-shot-classification
classification
calibrated-probabilities
routing
multiple-choice
tool-selection
jev
text-embeddings-inference
Instructions to use tardellirs/crivo-400m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tardellirs/crivo-400m with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="tardellirs/crivo-400m")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("tardellirs/crivo-400m") model = AutoModel.from_pretrained("tardellirs/crivo-400m", device_map="auto") - jev-style
How to use tardellirs/crivo-400m with jev-style:
pip install "jev-style[torch]"
from jev_style import JevStyle, noul, choice js = JevStyle.from_pretrained("tardellirs/crivo-400m") out = js.decide("I was charged twice for one order.", { "billing": noul("This message is about billing."), "team": choice("Which team should handle it?", ["billing", "shipping", "tech"]), }) print(out["answers"]["team"]["choice"]) - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from tardellirs/crivo-400m: direct link, hf CLI and curl.
- Browser
- Download file 3.58 MB
-
https://huggingface.co/tardellirs/crivo-400m/resolve/main/tokenizer.json
- Command line
-
hf download hf://tardellirs/crivo-400m/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/tardellirs/crivo-400m/resolve/main/tokenizer.json
3.58 MB
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