Instructions to use prajjwal1/albert-base-v1-mnli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use prajjwal1/albert-base-v1-mnli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="prajjwal1/albert-base-v1-mnli")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("prajjwal1/albert-base-v1-mnli") model = AutoModelForSequenceClassification.from_pretrained("prajjwal1/albert-base-v1-mnli") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d5b8012f8e699d3d77f5de53b53a2d9ffebf1e3055ef5ad98431ca2b0743389b
- Size of remote file:
- 46.7 MB
- SHA256:
- 37172aa16faabb9bda1bd88b23a831d010967c42f879800e9669b805b55fadfd
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