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")# pip install -U transformers accelerate # 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
File size: 228 Bytes
60af700 | 1 2 3 4 5 6 7 8 9 | {
"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
} |