Instructions to use Cheng98/opt-125m-rte with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Cheng98/opt-125m-rte with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Cheng98/opt-125m-rte")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Cheng98/opt-125m-rte") model = AutoModelForSequenceClassification.from_pretrained("Cheng98/opt-125m-rte") - Notebooks
- Google Colab
- Kaggle
| { | |
| "epoch": 5.0, | |
| "eval_accuracy": 0.6787003610108303, | |
| "eval_loss": 1.771389365196228, | |
| "eval_runtime": 0.7069, | |
| "eval_samples": 277, | |
| "eval_samples_per_second": 391.849, | |
| "eval_steps_per_second": 49.512, | |
| "train_loss": 0.3118787203079615, | |
| "train_runtime": 82.0111, | |
| "train_samples": 2490, | |
| "train_samples_per_second": 151.809, | |
| "train_steps_per_second": 9.511 | |
| } |