Instructions to use M-FAC/bert-tiny-finetuned-qqp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use M-FAC/bert-tiny-finetuned-qqp with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="M-FAC/bert-tiny-finetuned-qqp")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("M-FAC/bert-tiny-finetuned-qqp") model = AutoModelForSequenceClassification.from_pretrained("M-FAC/bert-tiny-finetuned-qqp", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e5904f736a4b19b8a8cabc0f06259648bd4147f4ca6f4cf9a73eda8349ed82be
- Size of remote file:
- 17.6 MB
- SHA256:
- 7dcb0b1610336e8a44671f28011e06e8ebf43b9137cb2d41b60378aa3cde2eaa
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