Text Classification
Transformers
PyTorch
TensorBoard
Safetensors
English
roberta
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use JeremiahZ/roberta-base-rte with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JeremiahZ/roberta-base-rte with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="JeremiahZ/roberta-base-rte")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("JeremiahZ/roberta-base-rte") model = AutoModelForSequenceClassification.from_pretrained("JeremiahZ/roberta-base-rte") - Notebooks
- Google Colab
- Kaggle
File size: 403 Bytes
3a00009 4625b60 3a00009 4625b60 3a00009 4625b60 3a00009 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 | {
"epoch": 10.0,
"eval_accuracy": 0.7978339350180506,
"eval_loss": 0.5446364283561707,
"eval_runtime": 0.6251,
"eval_samples": 277,
"eval_samples_per_second": 443.162,
"eval_steps_per_second": 55.995,
"train_loss": 0.31504388589125415,
"train_runtime": 295.0697,
"train_samples": 2490,
"train_samples_per_second": 84.387,
"train_steps_per_second": 5.287
} |