Instructions to use WeightWatcher/albert-large-v2-rte with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use WeightWatcher/albert-large-v2-rte with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="WeightWatcher/albert-large-v2-rte")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("WeightWatcher/albert-large-v2-rte") model = AutoModelForSequenceClassification.from_pretrained("WeightWatcher/albert-large-v2-rte", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 16e9644e05638445e6dc44b89f6b2c9beb116f47090c817707dd92591343cffe
- Size of remote file:
- 70.8 MB
- SHA256:
- 6a7920bf2beeb91f08c79d466e4977288401ddc36b8f55b996f4db4a1f0c3c6a
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