Text Classification
Transformers
PyTorch
English
bert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use Cheng98/bert-large-sst2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Cheng98/bert-large-sst2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Cheng98/bert-large-sst2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Cheng98/bert-large-sst2") model = AutoModelForSequenceClassification.from_pretrained("Cheng98/bert-large-sst2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "epoch": 5.0, | |
| "eval_accuracy": 0.9254587155963303, | |
| "eval_loss": 0.374822735786438, | |
| "eval_runtime": 2.241, | |
| "eval_samples": 872, | |
| "eval_samples_per_second": 389.107, | |
| "eval_steps_per_second": 48.638, | |
| "train_loss": 0.1226784371989923, | |
| "train_runtime": 1894.6082, | |
| "train_samples": 67349, | |
| "train_samples_per_second": 177.739, | |
| "train_steps_per_second": 11.11 | |
| } |