Instructions to use tzhao3/Bert-M-SST2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tzhao3/Bert-M-SST2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="tzhao3/Bert-M-SST2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("tzhao3/Bert-M-SST2") model = AutoModelForSequenceClassification.from_pretrained("tzhao3/Bert-M-SST2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download optimizer.pt from tzhao3/Bert-M-SST2: direct link, hf CLI and curl.
- Browser
- Download file 331 MB
-
https://huggingface.co/tzhao3/Bert-M-SST2/resolve/main/optimizer.pt
- Command line
-
hf download hf://tzhao3/Bert-M-SST2/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/tzhao3/Bert-M-SST2/resolve/main/optimizer.pt
331 MB
- Xet hash:
- 2a4fd9174ea5489d5ca15325c902aed44c9f05f053f3ef6f4b4c64707648a956
- Size of remote file:
- 331 MB
- SHA256:
- 9822d451c23c9d1a4219b5d731af3967b5694b24922818c968112db44e13a654
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.