Instructions to use 275Gameplay/test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use 275Gameplay/test with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="275Gameplay/test")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("275Gameplay/test") model = AutoModelForCTC.from_pretrained("275Gameplay/test", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download trainer_state.json from 275Gameplay/test: direct link, hf CLI and curl.
- Browser
- Download file 673 Bytes
-
https://huggingface.co/275Gameplay/test/resolve/main/trainer_state.json
- Command line
-
hf download hf://275Gameplay/test/trainer_state.json
-
curl -L -o trainer_state.json https://huggingface.co/275Gameplay/test/resolve/main/trainer_state.json
673 Bytes
| { | |
| "best_metric": null, | |
| "best_model_checkpoint": null, | |
| "epoch": 1.3239669421487603, | |
| "global_step": 400, | |
| "is_hyper_param_search": false, | |
| "is_local_process_zero": true, | |
| "is_world_process_zero": true, | |
| "log_history": [ | |
| { | |
| "epoch": 1.32, | |
| "learning_rate": 0.00023999999999999998, | |
| "loss": 6.7247, | |
| "step": 400 | |
| }, | |
| { | |
| "epoch": 1.32, | |
| "eval_loss": 3.472536087036133, | |
| "eval_runtime": 615.8884, | |
| "eval_samples_per_second": 3.553, | |
| "eval_wer": 1.0, | |
| "step": 400 | |
| } | |
| ], | |
| "max_steps": 3020, | |
| "num_train_epochs": 10, | |
| "total_flos": 8.196165988048397e+17, | |
| "trial_name": null, | |
| "trial_params": null | |
| } | |