Instructions to use Harsha901/tinybert-sst2-student with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Harsha901/tinybert-sst2-student with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Harsha901/tinybert-sst2-student")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Harsha901/tinybert-sst2-student") model = AutoModelForSequenceClassification.from_pretrained("Harsha901/tinybert-sst2-student", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4149adac62469e4b45ca2b9a0eed343aa450359c808d980703f38056f5b654ef
- Size of remote file:
- 57.4 MB
- SHA256:
- 9beec5ffe6ad07b15bc89fbc77b10a287b5a0c965cb1b47677e74ff8edbdabc4
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