Instructions to use dusersad12/SweepChampion-Release with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dusersad12/SweepChampion-Release with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="dusersad12/SweepChampion-Release")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("dusersad12/SweepChampion-Release") model = AutoModelForSequenceClassification.from_pretrained("dusersad12/SweepChampion-Release", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download hparams.json from dusersad12/SweepChampion-Release: direct link, hf CLI and curl.
- Browser
- Download file 275 Bytes
-
https://huggingface.co/dusersad12/SweepChampion-Release/resolve/main/hparams.json
- Command line
-
hf download hf://dusersad12/SweepChampion-Release/hparams.json
-
curl -L -o hparams.json https://huggingface.co/dusersad12/SweepChampion-Release/resolve/main/hparams.json
275 Bytes
| { | |
| "run_id": "run_20240902_b7e2", | |
| "project": "product-reviews-clf", | |
| "sweep_id": "swp_8f31f2", | |
| "status": "completed", | |
| "batch_size": 32, | |
| "epochs": 4, | |
| "learning_rate": 0.0002, | |
| "weight_decay": 0.02, | |
| "warmup_ratio": 0.1, | |
| "max_seq_length": 256, | |
| "seed": 1337 | |
| } | |