Instructions to use jonas/bert-base-uncased-finetuned-sdg 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 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")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("jonas/bert-base-uncased-finetuned-sdg") model = AutoModelForSequenceClassification.from_pretrained("jonas/bert-base-uncased-finetuned-sdg", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 19af7ac94322f38a121df1355c1da4495b7ecd3bf468ab8002109974d09324db
- Size of remote file:
- 3.38 kB
- SHA256:
- a7b3516ad8951747a16430e588c30a6ec7ea3cc461efcaabaf3e382fec9d22ed
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.