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
Download eval_results.json from JeremiahZ/bert-base-uncased-sst2: direct link, hf CLI and curl.
- Browser
- Download file 228 Bytes
-
https://huggingface.co/JeremiahZ/bert-base-uncased-sst2/resolve/refs%2Fpr%2F2/eval_results.json
- Command line
-
hf download hf://JeremiahZ/bert-base-uncased-sst2@refs/pr/2/eval_results.json
-
curl -L -o eval_results.json https://huggingface.co/JeremiahZ/bert-base-uncased-sst2/resolve/refs%2Fpr%2F2/eval_results.json
228 Bytes
| { | |
| "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 | |
| } |