Instructions to use Harsha901/distilbert-sst2-student with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Harsha901/distilbert-sst2-student with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Harsha901/distilbert-sst2-student")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Harsha901/distilbert-sst2-student") model = AutoModelForSequenceClassification.from_pretrained("Harsha901/distilbert-sst2-student", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- fe9ec3286259ef26c27f904b1f054fa1ad67c4c00a6076a711e410784b86efa7
- Size of remote file:
- 268 MB
- SHA256:
- 82a613b1cac39a908e1089b989ad9cd58856177e882d2974aa677e1de90c5afe
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