Instructions to use akashmaggon/Bert-sequence-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use akashmaggon/Bert-sequence-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="akashmaggon/Bert-sequence-classification")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("akashmaggon/Bert-sequence-classification") model = AutoModelForSequenceClassification.from_pretrained("akashmaggon/Bert-sequence-classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from akashmaggon/Bert-sequence-classification: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/akashmaggon/Bert-sequence-classification/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://akashmaggon/Bert-sequence-classification/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/akashmaggon/Bert-sequence-classification/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- d9c93d7df5e1b0efcfb387c0acf3f1823b427c41655761d2b84516710991b786
- Size of remote file:
- 438 MB
- SHA256:
- 4def61d457a9655f4f89f5a95aee5a8196320678eca3c1b0781f71644fc66a40
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