Instructions to use Cheng98/opt-125m-mnli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Cheng98/opt-125m-mnli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Cheng98/opt-125m-mnli")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Cheng98/opt-125m-mnli") model = AutoModelForSequenceClassification.from_pretrained("Cheng98/opt-125m-mnli", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 655 Bytes
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"epoch": 5.0,
"epoch_mm": 5.0,
"eval_accuracy": 0.8208863983698421,
"eval_accuracy_mm": 0.8217046379170057,
"eval_loss": 1.2960911989212036,
"eval_loss_mm": 1.273514986038208,
"eval_runtime": 13.5254,
"eval_runtime_mm": 13.4557,
"eval_samples": 9815,
"eval_samples_mm": 9832,
"eval_samples_per_second": 725.672,
"eval_samples_per_second_mm": 730.694,
"eval_steps_per_second": 90.718,
"eval_steps_per_second_mm": 91.337,
"train_loss": 0.2862651685536918,
"train_runtime": 5523.0542,
"train_samples": 392702,
"train_samples_per_second": 355.512,
"train_steps_per_second": 22.22
} |