Instructions to use AnonARR/qqp-bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AnonARR/qqp-bert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="AnonARR/qqp-bert")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("AnonARR/qqp-bert") model = AutoModelForSequenceClassification.from_pretrained("AnonARR/qqp-bert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6795bcc51b51dac9caa2090ee8c58057b796ef6037ab19512cc62b261376e4df
- Size of remote file:
- 433 MB
- SHA256:
- 3fdb0d1efd99240cbd045f687a8d4823caac16c22d739dbb093facb4ab2dbe6a
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