Instructions to use rcorkill/DeBERTRAMa-SST2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rcorkill/DeBERTRAMa-SST2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="rcorkill/DeBERTRAMa-SST2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("rcorkill/DeBERTRAMa-SST2") model = AutoModelForSequenceClassification.from_pretrained("rcorkill/DeBERTRAMa-SST2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download optimizer.pt from rcorkill/DeBERTRAMa-SST2: direct link, hf CLI and curl.
- Browser
- Download file 1.12 GB
-
https://huggingface.co/rcorkill/DeBERTRAMa-SST2/resolve/main/optimizer.pt
- Command line
-
hf download hf://rcorkill/DeBERTRAMa-SST2/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/rcorkill/DeBERTRAMa-SST2/resolve/main/optimizer.pt
1.12 GB
- Xet hash:
- 59ad758181aec4c3c03a6fc09a11b02baca2e8f24df1b5447ceeee7e97fe9faa
- Size of remote file:
- 1.12 GB
- SHA256:
- 7d7083ab797d27e1496e15a60015b708356b9dc97e248a2270a4b9207e9b4f2b
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