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")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("sinancavdar/BertForSequenceClassification") model = AutoModelForSequenceClassification.from_pretrained("sinancavdar/BertForSequenceClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f1a80865b82e0a6483050f50a68e7a22c89a001bd62b582504d3a1aa6081f830
- Size of remote file:
- 438 MB
- SHA256:
- dbefe60beb35254768e10d3069b7ccd900afdd19758911c017c5228a35aa1aac
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