Text Classification
Transformers
PyTorch
bert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use ncats/EpiClassify4GARD with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ncats/EpiClassify4GARD with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ncats/EpiClassify4GARD")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ncats/EpiClassify4GARD") model = AutoModelForSequenceClassification.from_pretrained("ncats/EpiClassify4GARD", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "best_metric": null, | |
| "best_model_checkpoint": null, | |
| "epoch": 4.0, | |
| "global_step": 252, | |
| "is_hyper_param_search": false, | |
| "is_local_process_zero": true, | |
| "is_world_process_zero": true, | |
| "log_history": [ | |
| { | |
| "epoch": 4.0, | |
| "step": 252, | |
| "total_flos": 1014634340562720.0, | |
| "train_loss": 0.06538131501939562, | |
| "train_runtime": 127.4981, | |
| "train_samples_per_second": 31.373, | |
| "train_steps_per_second": 1.976 | |
| } | |
| ], | |
| "max_steps": 252, | |
| "num_train_epochs": 4, | |
| "total_flos": 1014634340562720.0, | |
| "trial_name": null, | |
| "trial_params": null | |
| } | |