Text Classification
Transformers
PyTorch
deberta-v2
Generated from Trainer
text-embeddings-inference
Instructions to use bdpc/DeBERT_50K_steps with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bdpc/DeBERT_50K_steps with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="bdpc/DeBERT_50K_steps")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("bdpc/DeBERT_50K_steps") model = AutoModelForSequenceClassification.from_pretrained("bdpc/DeBERT_50K_steps", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from bdpc/DeBERT_50K_steps: direct link, hf CLI and curl.
- Browser
- Download file 8.66 MB
-
https://huggingface.co/bdpc/DeBERT_50K_steps/resolve/refs%2Fpr%2F1/tokenizer.json
- Command line
-
hf download hf://bdpc/DeBERT_50K_steps@refs/pr/1/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/bdpc/DeBERT_50K_steps/resolve/refs%2Fpr%2F1/tokenizer.json
8.66 MB
File too large to display, you can check the raw version instead.