Text Classification
Transformers
TensorBoard
Safetensors
bert
Gwak
10_class
Generated from Trainer
text-embeddings-inference
Instructions to use Gwak1/model_output with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Gwak1/model_output with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Gwak1/model_output")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Gwak1/model_output") model = AutoModelForSequenceClassification.from_pretrained("Gwak1/model_output", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Gwak1/model_output: direct link, hf CLI and curl.
- Browser
- Download file 5.37 kB
-
https://huggingface.co/Gwak1/model_output/resolve/main/training_args.bin
- Command line
-
hf download hf://Gwak1/model_output/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Gwak1/model_output/resolve/main/training_args.bin
5.37 kB
- Xet hash:
- 24d1ffd1a1d72538e26465a4dbcc98d6336616f8c61a8d209656d9b45a591885
- Size of remote file:
- 5.37 kB
- SHA256:
- 9d3d7d0ad141c9382b1063124456f8d9798223ebcef0e90a1df645b63eca4649
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