Text Classification
Transformers
PyTorch
English
bert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use Intel/MiniLM-L12-H384-uncased-mrpc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Intel/MiniLM-L12-H384-uncased-mrpc with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Intel/MiniLM-L12-H384-uncased-mrpc")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Intel/MiniLM-L12-H384-uncased-mrpc") model = AutoModelForSequenceClassification.from_pretrained("Intel/MiniLM-L12-H384-uncased-mrpc", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download train_results.json from Intel/MiniLM-L12-H384-uncased-mrpc: direct link, hf CLI and curl.
- Browser
- Download file 194 Bytes
-
https://huggingface.co/Intel/MiniLM-L12-H384-uncased-mrpc/resolve/f6b72c04dfbfe19ccadab2701bcc72e2efa7a12c/train_results.json
- Command line
-
hf download hf://Intel/MiniLM-L12-H384-uncased-mrpc@f6b72c04dfbfe19ccadab2701bcc72e2efa7a12c/train_results.json
-
curl -L -o train_results.json https://huggingface.co/Intel/MiniLM-L12-H384-uncased-mrpc/resolve/f6b72c04dfbfe19ccadab2701bcc72e2efa7a12c/train_results.json
194 Bytes
| { | |
| "epoch": 5.0, | |
| "train_loss": 0.29514418560525646, | |
| "train_runtime": 616.8399, | |
| "train_samples": 3668, | |
| "train_samples_per_second": 29.732, | |
| "train_steps_per_second": 1.864 | |
| } |