Text Classification
Transformers
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use matmei/BinaryPOQC with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use matmei/BinaryPOQC with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="matmei/BinaryPOQC")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("matmei/BinaryPOQC") model = AutoModelForSequenceClassification.from_pretrained("matmei/BinaryPOQC", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from matmei/BinaryPOQC: direct link, hf CLI and curl.
- Browser
- Download file 5.33 kB
-
https://huggingface.co/matmei/BinaryPOQC/resolve/main/training_args.bin
- Command line
-
hf download hf://matmei/BinaryPOQC/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/matmei/BinaryPOQC/resolve/main/training_args.bin
5.33 kB
- Xet hash:
- f09e420a838b68a31f60e6bc6553e5854fec59956e7c813a13045a5672477e12
- Size of remote file:
- 5.33 kB
- SHA256:
- 37849f92fd0a37bfbfee8a8e45e9270d6a536a4dd76a06470fb011e9013ac0f1
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