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 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lCREEl/pick-150m with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="lCREEl/pick-150m")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("lCREEl/pick-150m") model = AutoModel.from_pretrained("lCREEl/pick-150m", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 393 Bytes
195c694 2a6e76d 195c694 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 | {
"args": {
"architecture": "pipeline",
"model": "cross-encoder/ettin-reranker-150m-v1",
"max_length": 7999,
"packing_version": 2,
"packing": "shared-state",
"question_reads_options": true,
"state_reads_questions": true,
"source_checkpoint": "/Users/leejongmin/code/DecisionTransformer/checkpoints/pick-150m-v5/v5_stage2_best.pt",
"source_step": 75
}
}
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