Text Classification
Transformers
PyTorch
TensorBoard
English
hybridbert
Generated from Trainer
Eval Results (legacy)
Instructions to use gokuls/add_BERT_48_mrpc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gokuls/add_BERT_48_mrpc with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="gokuls/add_BERT_48_mrpc")# Load model directly from transformers import AutoModelForSequenceClassification model = AutoModelForSequenceClassification.from_pretrained("gokuls/add_BERT_48_mrpc", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1af901c1dd9a5b611a6a0784e92ec4cc8d59c37861bbd749e0d3a3b5f98c292f
- Size of remote file:
- 3.96 kB
- SHA256:
- a83a52920ad0193b70cbd781d06809fa109e9e5109fd8ab91167d207e5c7f081
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