Text Classification
Transformers
PyTorch
English
bert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use junzai/demo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use junzai/demo with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="junzai/demo")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("junzai/demo") model = AutoModelForSequenceClassification.from_pretrained("junzai/demo", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download train_results.json from junzai/demo: direct link, hf CLI and curl.
- Browser
- Download file 193 Bytes
-
https://huggingface.co/junzai/demo/resolve/main/train_results.json
- Command line
-
hf download hf://junzai/demo/train_results.json
-
curl -L -o train_results.json https://huggingface.co/junzai/demo/resolve/main/train_results.json
193 Bytes
| { | |
| "epoch": 1.0, | |
| "train_loss": 0.5285058779913875, | |
| "train_runtime": 1821.1561, | |
| "train_samples": 3668, | |
| "train_samples_per_second": 2.014, | |
| "train_steps_per_second": 0.252 | |
| } |