Instructions to use Greg1901/BertSummaDev_AFD with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Greg1901/BertSummaDev_AFD with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Greg1901/BertSummaDev_AFD")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Greg1901/BertSummaDev_AFD") model = AutoModelForSequenceClassification.from_pretrained("Greg1901/BertSummaDev_AFD", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Greg1901/BertSummaDev_AFD: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/Greg1901/BertSummaDev_AFD/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Greg1901/BertSummaDev_AFD/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Greg1901/BertSummaDev_AFD/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- 80ede2ba898add69c2210b3228719be8ee79b88dd0910943d9e4e8a21221b237
- Size of remote file:
- 438 MB
- SHA256:
- 84a4fc09e7b3fb5b3ba38d132e6b299b756b5949bd2a4c80185d777336ca488d
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