Text Classification
Transformers
Safetensors
jev-style
English
modernbert
feature-extraction
decision-model
typed-decisions
system-one
decision-index
knowledge-distillation
calibration
ettin
cross-encoder
zero-shot-classification
classification
calibrated-probabilities
routing
multiple-choice
tool-selection
jev
Eval Results (legacy)
text-embeddings-inference
Instructions to use tardellirs/crivo-150m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tardellirs/crivo-150m with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="tardellirs/crivo-150m")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("tardellirs/crivo-150m") model = AutoModel.from_pretrained("tardellirs/crivo-150m", device_map="auto") - jev-style
How to use tardellirs/crivo-150m with jev-style:
pip install "jev-style[torch]"
from jev_style import JevStyle, noul, choice js = JevStyle.from_pretrained("tardellirs/crivo-150m") 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 model.safetensors from tardellirs/crivo-150m: direct link, hf CLI and curl.
- Browser
- Download file 598 MB
-
https://huggingface.co/tardellirs/crivo-150m/resolve/main/model.safetensors
- Command line
-
hf download hf://tardellirs/crivo-150m/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/tardellirs/crivo-150m/resolve/main/model.safetensors
598 MB
- Xet hash:
- a02212b7acd983a3b3b9d8a2b38b95fe17a5b54010d501430dd3e40b4c71773b
- Size of remote file:
- 598 MB
- SHA256:
- 9022e370bb7ddf91297a64c0b538540acdd5c09dee5848ea854bb177577daab8
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