Instructions to use sinancavdar/BertForSequenceClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sinancavdar/BertForSequenceClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="sinancavdar/BertForSequenceClassification")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("sinancavdar/BertForSequenceClassification") model = AutoModelForSequenceClassification.from_pretrained("sinancavdar/BertForSequenceClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from sinancavdar/BertForSequenceClassification: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/sinancavdar/BertForSequenceClassification/resolve/refs%2Fpr%2F1/pytorch_model.bin
- Command line
-
hf download hf://sinancavdar/BertForSequenceClassification@refs/pr/1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/sinancavdar/BertForSequenceClassification/resolve/refs%2Fpr%2F1/pytorch_model.bin
438 MB
- Xet hash:
- b9984803b80bdc9de7bbdcee4c719f72e676063d445cba3ca23a5e9b2daaae36
- Size of remote file:
- 438 MB
- SHA256:
- f73661c04d422a80fbb73c04fad9862d96eb1aaca14312dd6222c9e0e93f76bf
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