Text Classification
Transformers
PyTorch
Safetensors
bert
Generated from Trainer
text-embeddings-inference
Instructions to use jonas/bert-base-uncased-finetuned-sdg-Mar23 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jonas/bert-base-uncased-finetuned-sdg-Mar23 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="jonas/bert-base-uncased-finetuned-sdg-Mar23")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("jonas/bert-base-uncased-finetuned-sdg-Mar23") model = AutoModelForSequenceClassification.from_pretrained("jonas/bert-base-uncased-finetuned-sdg-Mar23", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from jonas/bert-base-uncased-finetuned-sdg-Mar23: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/jonas/bert-base-uncased-finetuned-sdg-Mar23/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://jonas/bert-base-uncased-finetuned-sdg-Mar23/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/jonas/bert-base-uncased-finetuned-sdg-Mar23/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- 548d5ef19f64239ca7643e53a37b1c23f976cee2fe3bebd65e6503a95d650c41
- Size of remote file:
- 438 MB
- SHA256:
- fa2787b689b7e755867cde9c5e064b746d3962042f10783b87e5412361de37cd
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