Text Classification
Transformers
Safetensors
bert
multilingual
multi-label-classification
community-notes
topic-classification
text-embeddings-inference
Instructions to use ychuai/community-notes-topic-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ychuai/community-notes-topic-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ychuai/community-notes-topic-classifier")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ychuai/community-notes-topic-classifier") model = AutoModelForSequenceClassification.from_pretrained("ychuai/community-notes-topic-classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download validation_metrics.json from ychuai/community-notes-topic-classifier: direct link, hf CLI and curl.
- Browser
- Download file 271 Bytes
-
https://huggingface.co/ychuai/community-notes-topic-classifier/resolve/main/validation_metrics.json
- Command line
-
hf download hf://ychuai/community-notes-topic-classifier/validation_metrics.json
-
curl -L -o validation_metrics.json https://huggingface.co/ychuai/community-notes-topic-classifier/resolve/main/validation_metrics.json
271 Bytes
| { | |
| "eval_loss": 0.1876334697008133, | |
| "eval_f1_micro": 0.8312189180960113, | |
| "eval_f1_macro": 0.8093773315390159, | |
| "eval_f1_samples": 0.7618056223611779, | |
| "eval_runtime": 65.3206, | |
| "eval_samples_per_second": 45.468, | |
| "eval_steps_per_second": 11.375, | |
| "epoch": 5.0 | |
| } |