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 pytorch_model.bin from lindeberg/tiny-bert-sst2-distilled: direct link, hf CLI and curl.
- Browser
- Download file 17.6 MB
-
https://huggingface.co/lindeberg/tiny-bert-sst2-distilled/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://lindeberg/tiny-bert-sst2-distilled/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/lindeberg/tiny-bert-sst2-distilled/resolve/main/pytorch_model.bin
17.6 MB
- Xet hash:
- 9f5e2ca679d4415948ea2ce171a6637bb877063611af87e979abd8ad848a84b9
- Size of remote file:
- 17.6 MB
- SHA256:
- 22adc5306fd9e0d24ebfa35bff6540223929846e67313869f28509611a7191ec
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