Instructions to use textattack/albert-base-v2-QQP with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use textattack/albert-base-v2-QQP with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="textattack/albert-base-v2-QQP")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("textattack/albert-base-v2-QQP") model = AutoModelForSequenceClassification.from_pretrained("textattack/albert-base-v2-QQP", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a0053c762240446f85ab13f5e60f43100af61bd7ab23eac15d7166470975378a
- Size of remote file:
- 46.7 MB
- SHA256:
- 9dd8fd311699ec400275e4ad7f768b3326b8bbefe01f974482a58402305cd72d
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