Instructions to use kagented/bert-base-finetuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kagented/bert-base-finetuned with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="kagented/bert-base-finetuned")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("kagented/bert-base-finetuned") model = AutoModelForSequenceClassification.from_pretrained("kagented/bert-base-finetuned", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from kagented/bert-base-finetuned: direct link, hf CLI and curl.
- Browser
- Download file 443 MB
-
https://huggingface.co/kagented/bert-base-finetuned/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://kagented/bert-base-finetuned/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/kagented/bert-base-finetuned/resolve/main/pytorch_model.bin
443 MB
- Xet hash:
- e93b8a06c61736c97165a4db444265dd93e214a6dcb0838d1981f279f88724fb
- Size of remote file:
- 443 MB
- SHA256:
- 8ad74b5160b96c952db1a11f0758f105a06aea295e982c029ddc5a5c54dd2a8a
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