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:
- 5eb5ddf444790874df259add719619c7253ad75103d96596a097eff9f24c836d
- Size of remote file:
- 57.4 MB
- SHA256:
- bc7ba9745c0e65dc79dcb2a66485813865659ea53908441ea368d80d860acdde
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