Text Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
text-embeddings-inference
Instructions to use ultra-coder54732/MiniLM-prop-16-train-set with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ultra-coder54732/MiniLM-prop-16-train-set with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ultra-coder54732/MiniLM-prop-16-train-set")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ultra-coder54732/MiniLM-prop-16-train-set") model = AutoModelForSequenceClassification.from_pretrained("ultra-coder54732/MiniLM-prop-16-train-set", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from ultra-coder54732/MiniLM-prop-16-train-set: direct link, hf CLI and curl.
- Browser
- Download file 3.38 kB
-
https://huggingface.co/ultra-coder54732/MiniLM-prop-16-train-set/resolve/refs%2Fpr%2F2/training_args.bin
- Command line
-
hf download hf://ultra-coder54732/MiniLM-prop-16-train-set@refs/pr/2/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/ultra-coder54732/MiniLM-prop-16-train-set/resolve/refs%2Fpr%2F2/training_args.bin
3.38 kB
- Xet hash:
- 9cca6e1fb9a9a6bf3141e7f42ae3016072f866854b24fe65b72012597f954c9e
- Size of remote file:
- 3.38 kB
- SHA256:
- c133759038dcc1291630efd564bf761a210903360715eb5d75b85ba6ab14b5ce
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.