Instructions to use moshew/bert-mini-sst2-distilled with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use moshew/bert-mini-sst2-distilled with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="moshew/bert-mini-sst2-distilled")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("moshew/bert-mini-sst2-distilled") model = AutoModelForSequenceClassification.from_pretrained("moshew/bert-mini-sst2-distilled", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 347f2ff1bac6a0237a335d5f859ff806e5cc5df8232db80128f7bcc23354a701
- Size of remote file:
- 44.7 MB
- SHA256:
- cfa971d8ff446e64bca4be9d4be95d73bfd3e638bd02f707b5cc0e24bda88d98
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