Text Classification
Transformers
Safetensors
English
modernbert
feature-extraction
decision-model
decision-transformer
ettin
cross-encoder
zero-shot-classification
classification
calibrated-probabilities
routing
multiple-choice
tool-calling
agent-decision
text-embeddings-inference
Instructions to use lCREEl/pick-150m-runpod with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lCREEl/pick-150m-runpod with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="lCREEl/pick-150m-runpod")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("lCREEl/pick-150m-runpod") model = AutoModel.from_pretrained("lCREEl/pick-150m-runpod", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Ctrl+K
- data
- docs
- export-v5
- reports
- runs
- scripts
- stages
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