Instructions to use Intel/xlnet-base-cased-mrpc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Intel/xlnet-base-cased-mrpc with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Intel/xlnet-base-cased-mrpc")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Intel/xlnet-base-cased-mrpc") model = AutoModelForSequenceClassification.from_pretrained("Intel/xlnet-base-cased-mrpc", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "epoch": 5.0, | |
| "eval_accuracy": 0.8455882352941176, | |
| "eval_combined_score": 0.867627742865973, | |
| "eval_f1": 0.8896672504378283, | |
| "eval_loss": 0.7156228423118591, | |
| "eval_runtime": 28.4059, | |
| "eval_samples": 408, | |
| "eval_samples_per_second": 14.363, | |
| "eval_steps_per_second": 1.795, | |
| "train_loss": 0.2684407242484715, | |
| "train_runtime": 5649.8451, | |
| "train_samples": 3668, | |
| "train_samples_per_second": 3.246, | |
| "train_steps_per_second": 0.204 | |
| } |