Instructions to use M-FAC/bert-mini-finetuned-qqp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use M-FAC/bert-mini-finetuned-qqp with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="M-FAC/bert-mini-finetuned-qqp")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("M-FAC/bert-mini-finetuned-qqp") model = AutoModelForSequenceClassification.from_pretrained("M-FAC/bert-mini-finetuned-qqp", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from M-FAC/bert-mini-finetuned-qqp: direct link, hf CLI and curl.
- Browser
- Download file 44.7 MB
-
https://huggingface.co/M-FAC/bert-mini-finetuned-qqp/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://M-FAC/bert-mini-finetuned-qqp/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/M-FAC/bert-mini-finetuned-qqp/resolve/main/pytorch_model.bin
44.7 MB
- Xet hash:
- 9b29496ea966de2d643ec11a2ca9b0aad45fe01544512c160246775fa3b2a432
- Size of remote file:
- 44.7 MB
- SHA256:
- 0e828849ba49963fe3f07137ddcc7ace6b4e0435b32d2b3eb512152f131c2c17
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