Instructions to use WeightWatcher/albert-large-v2-sst2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use WeightWatcher/albert-large-v2-sst2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="WeightWatcher/albert-large-v2-sst2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("WeightWatcher/albert-large-v2-sst2") model = AutoModelForSequenceClassification.from_pretrained("WeightWatcher/albert-large-v2-sst2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 06b470fd8074b04e4165dd1f187bd2130b4fd5dc47412ce2ec077affec8e3dab
- Size of remote file:
- 70.8 MB
- SHA256:
- eea926513ae7a0c1f183831b8475b26c164e965e610f501add695c7e005f81e9
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