Instructions to use CodeHima/TOSBertV2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CodeHima/TOSBertV2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="CodeHima/TOSBertV2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("CodeHima/TOSBertV2") model = AutoModelForSequenceClassification.from_pretrained("CodeHima/TOSBertV2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from CodeHima/TOSBertV2: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/CodeHima/TOSBertV2/resolve/main/model.safetensors
- Command line
-
hf download hf://CodeHima/TOSBertV2/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/CodeHima/TOSBertV2/resolve/main/model.safetensors
438 MB
- Xet hash:
- d86ff0530770412e1e97a963c93771fef6e63314335e4bbd67565e5e3c308c2e
- Size of remote file:
- 438 MB
- SHA256:
- 038429be298151ef4547dd2efa1d1db1a79e677481b487ada337ee31c95c0d05
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