Text Classification
Transformers
PyTorch
bert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use lindeberg/tiny-bert-sst2-distilled with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lindeberg/tiny-bert-sst2-distilled with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="lindeberg/tiny-bert-sst2-distilled")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("lindeberg/tiny-bert-sst2-distilled") model = AutoModelForSequenceClassification.from_pretrained("lindeberg/tiny-bert-sst2-distilled", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from lindeberg/tiny-bert-sst2-distilled: direct link, hf CLI and curl.
- Browser
- Download file 711 kB
-
https://huggingface.co/lindeberg/tiny-bert-sst2-distilled/resolve/main/tokenizer.json
- Command line
-
hf download hf://lindeberg/tiny-bert-sst2-distilled/tokenizer.json
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curl -L -o tokenizer.json https://huggingface.co/lindeberg/tiny-bert-sst2-distilled/resolve/main/tokenizer.json
711 kB
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