Text Classification
Transformers
PyTorch
deberta-v2
Generated from Trainer
text-embeddings-inference
Instructions to use keremp/deberta-em with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use keremp/deberta-em with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="keremp/deberta-em")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("keremp/deberta-em") model = AutoModelForSequenceClassification.from_pretrained("keremp/deberta-em", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download eval_results.json from keremp/deberta-em: direct link, hf CLI and curl.
- Browser
- Download file 225 Bytes
-
https://huggingface.co/keremp/deberta-em/resolve/main/eval_results.json
- Command line
-
hf download hf://keremp/deberta-em/eval_results.json
-
curl -L -o eval_results.json https://huggingface.co/keremp/deberta-em/resolve/main/eval_results.json
225 Bytes
| { | |
| "epoch": 2.98, | |
| "eval_f1": 0.9369369369369368, | |
| "eval_loss": 0.07872804254293442, | |
| "eval_runtime": 11.6738, | |
| "eval_samples": 1439, | |
| "eval_samples_per_second": 123.268, | |
| "eval_steps_per_second": 3.855 | |
| } |