Text Classification
Transformers
PyTorch
English
deberta-v2
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use Intel/deberta-v3-base-mrpc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Intel/deberta-v3-base-mrpc with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Intel/deberta-v3-base-mrpc")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Intel/deberta-v3-base-mrpc") model = AutoModelForSequenceClassification.from_pretrained("Intel/deberta-v3-base-mrpc", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download train_results.json from Intel/deberta-v3-base-mrpc: direct link, hf CLI and curl.
- Browser
- Download file 193 Bytes
-
https://huggingface.co/Intel/deberta-v3-base-mrpc/resolve/main/train_results.json
- Command line
-
hf download hf://Intel/deberta-v3-base-mrpc/train_results.json
-
curl -L -o train_results.json https://huggingface.co/Intel/deberta-v3-base-mrpc/resolve/main/train_results.json
193 Bytes
| { | |
| "epoch": 5.0, | |
| "train_loss": 0.2895339061902917, | |
| "train_runtime": 312.3775, | |
| "train_samples": 3668, | |
| "train_samples_per_second": 58.711, | |
| "train_steps_per_second": 3.681 | |
| } |