Text Classification
Transformers
PyTorch
TensorBoard
Safetensors
English
bert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use JeremiahZ/bert-base-uncased-sst2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JeremiahZ/bert-base-uncased-sst2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="JeremiahZ/bert-base-uncased-sst2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("JeremiahZ/bert-base-uncased-sst2") model = AutoModelForSequenceClassification.from_pretrained("JeremiahZ/bert-base-uncased-sst2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "epoch": 3.0, | |
| "eval_accuracy": 0.9323394495412844, | |
| "eval_loss": 0.2478291541337967, | |
| "eval_runtime": 3.9031, | |
| "eval_samples": 872, | |
| "eval_samples_per_second": 223.413, | |
| "eval_steps_per_second": 27.927 | |
| } |