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")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("bdpc/DeBERT_50K_steps") model = AutoModelForSequenceClassification.from_pretrained("bdpc/DeBERT_50K_steps") - Notebooks
- Google Colab
- Kaggle
| { | |
| "epoch": 0.4, | |
| "train_loss": 0.030178942489624022, | |
| "train_runtime": 76019.1937, | |
| "train_samples_per_second": 10.524, | |
| "train_steps_per_second": 0.658 | |
| } |