Instructions to use cambridgeltl/sst_mobilebert-uncased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cambridgeltl/sst_mobilebert-uncased with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="cambridgeltl/sst_mobilebert-uncased")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("cambridgeltl/sst_mobilebert-uncased") model = AutoModelForSequenceClassification.from_pretrained("cambridgeltl/sst_mobilebert-uncased", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from cambridgeltl/sst_mobilebert-uncased: direct link, hf CLI and curl.
- Browser
- Download file 98.8 MB
-
https://huggingface.co/cambridgeltl/sst_mobilebert-uncased/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://cambridgeltl/sst_mobilebert-uncased/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/cambridgeltl/sst_mobilebert-uncased/resolve/main/pytorch_model.bin
98.8 MB
- Xet hash:
- 996a81f15064b2b85a008b89d320d22118c2bcd9a27d66cde4a267a311900ccf
- Size of remote file:
- 98.8 MB
- SHA256:
- 72cf8e72d92ff3c9a38620079e255f95ff17cae5022e5c57c8cfc3618cfae048
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