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
Download run.json from lCREEl/pick-150m: direct link, hf CLI and curl.
- Browser
- Download file 393 Bytes
-
https://huggingface.co/lCREEl/pick-150m/resolve/main/run.json
- Command line
-
hf download hf://lCREEl/pick-150m/run.json
-
curl -L -o run.json https://huggingface.co/lCREEl/pick-150m/resolve/main/run.json
393 Bytes
| { | |
| "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 | |
| } | |
| } | |