Instructions to use sofom/bert-base-uncased-multitudev2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sofom/bert-base-uncased-multitudev2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="sofom/bert-base-uncased-multitudev2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("sofom/bert-base-uncased-multitudev2") model = AutoModelForSequenceClassification.from_pretrained("sofom/bert-base-uncased-multitudev2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from sofom/bert-base-uncased-multitudev2: direct link, hf CLI and curl.
- Browser
- Download file 219 MB
-
https://huggingface.co/sofom/bert-base-uncased-multitudev2/resolve/main/model.safetensors
- Command line
-
hf download hf://sofom/bert-base-uncased-multitudev2/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/sofom/bert-base-uncased-multitudev2/resolve/main/model.safetensors
219 MB
- Xet hash:
- 4e566b06366668f47c45acf2a23765e55e44aaa9a67c44eb61e5b862a0c7714f
- Size of remote file:
- 219 MB
- SHA256:
- fc875a1a4e1e8dd178983457ff99e54feb58bc85ce2c8d1085e240605540ecb5
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