Instructions to use prajjwal1/albert-base-v2-mnli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use prajjwal1/albert-base-v2-mnli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="prajjwal1/albert-base-v2-mnli")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("prajjwal1/albert-base-v2-mnli") model = AutoModelForSequenceClassification.from_pretrained("prajjwal1/albert-base-v2-mnli", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 09703faeca81c5a6798e7a1fa12a7d97594765932d33b3d21b7a9dc4dbf7332d
- Size of remote file:
- 1.82 GB
- SHA256:
- a2582142b23d4cfdedd1d48e0b5efd5f6ebdd2dee05bd54e0e4c8f759f122be1
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