Instructions to use devrunner09/classification-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use devrunner09/classification-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="devrunner09/classification-model")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("devrunner09/classification-model") model = AutoModel.from_pretrained("devrunner09/classification-model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from devrunner09/classification-model: direct link, hf CLI and curl.
- Browser
- Download file 1.11 GB
-
https://huggingface.co/devrunner09/classification-model/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://devrunner09/classification-model/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/devrunner09/classification-model/resolve/main/pytorch_model.bin
1.11 GB
- Xet hash:
- 85cecf236becde4ee433f99922fd5ebc0262f267ba10ccee2d334b3d0a7d9d53
- Size of remote file:
- 1.11 GB
- SHA256:
- d3f5d9226ec1be65a3a10b1c645d92861763bd3c62bdc975bf29d03fe4b287a3
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