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 pytorch_model.bin from ultra-coder54732/MiniLM-prop-16-train-set: direct link, hf CLI and curl.
- Browser
- Download file 134 MB
-
https://huggingface.co/ultra-coder54732/MiniLM-prop-16-train-set/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://ultra-coder54732/MiniLM-prop-16-train-set/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/ultra-coder54732/MiniLM-prop-16-train-set/resolve/main/pytorch_model.bin
134 MB
- Xet hash:
- 5e07ab742a414b8861351e3645e50efbf02e5741a17d27216a85d68eb6c317fa
- Size of remote file:
- 134 MB
- SHA256:
- 273207c6a09df865137cfc369aac36a7ee67b39fb9fc563b5b7869ac437e7505
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