Text Classification
Transformers
PyTorch
English
bert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use Cheng98/bert-base-sst2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Cheng98/bert-base-sst2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Cheng98/bert-base-sst2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Cheng98/bert-base-sst2") model = AutoModelForSequenceClassification.from_pretrained("Cheng98/bert-base-sst2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "epoch": 5.0, | |
| "eval_accuracy": 0.9151376146788991, | |
| "eval_loss": 0.46023672819137573, | |
| "eval_runtime": 1.2889, | |
| "eval_samples": 872, | |
| "eval_samples_per_second": 676.559, | |
| "eval_steps_per_second": 84.57, | |
| "train_loss": 0.111734983768146, | |
| "train_runtime": 1050.6373, | |
| "train_samples": 67349, | |
| "train_samples_per_second": 320.515, | |
| "train_steps_per_second": 20.035 | |
| } |