Instructions to use textattack/albert-base-v2-SST-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use textattack/albert-base-v2-SST-2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="textattack/albert-base-v2-SST-2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("textattack/albert-base-v2-SST-2") model = AutoModelForSequenceClassification.from_pretrained("textattack/albert-base-v2-SST-2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- df22845ae2ac5a651a733dec487669cfd575440b3762b05820e71844acf5481c
- Size of remote file:
- 46.7 MB
- SHA256:
- ad61f77feed66af4b11be45fe986f4257a8472e008a545a4812f70bc0862c934
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