Instructions to use M-FAC/bert-mini-finetuned-mrpc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use M-FAC/bert-mini-finetuned-mrpc with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="M-FAC/bert-mini-finetuned-mrpc")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("M-FAC/bert-mini-finetuned-mrpc") model = AutoModelForSequenceClassification.from_pretrained("M-FAC/bert-mini-finetuned-mrpc", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3fc16c618c2c74568aa838de35325d31dafacaff7a528654443d40a9c45963e2
- Size of remote file:
- 44.7 MB
- SHA256:
- 0008404230c043c6c0b33aa58826f0d347ae694269423852e812ead1eac8046a
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