Instructions to use prelington/OrcaleSeek with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- fastText
How to use prelington/OrcaleSeek with fastText:
from huggingface_hub import hf_hub_download import fasttext model = fasttext.load_model(hf_hub_download("prelington/OrcaleSeek", "model.bin")) - Notebooks
- Google Colab
- Kaggle
| import logging | |
| import wandb | |
| from transformers import TrainerCallback | |
| # Setup logging | |
| logging.basicConfig( | |
| level=logging.INFO, | |
| format='%(asctime)s - %(name)s - %(levelname)s - %(message)s', | |
| handlers=[ | |
| logging.FileHandler('training.log'), | |
| logging.StreamHandler() | |
| ] | |
| ) | |
| class CustomCallback(TrainerCallback): | |
| def on_log(self, args, state, control, logs=None, **kwargs): | |
| if logs: | |
| logging.info(f"Step {state.global_step}: {logs}") | |
| def on_epoch_end(self, args, state, control, **kwargs): | |
| logging.info(f"Epoch {state.epoch} completed") | |
| def setup_wandb(): | |
| wandb.init(project="OrcaleSeek", entity="your-username") | |
| wandb.config = { | |
| "learning_rate": 2e-5, | |
| "architecture": "OrcaleSeek", | |
| "dataset": "Your-Dataset", | |
| } |