Instructions to use Pragformer/PragFormer_private with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Pragformer/PragFormer_private with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Pragformer/PragFormer_private", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Pragformer/PragFormer_private", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
-
Upload BERT_Arch
f7d2089 -
Upload BERT_Arch
1afbefb -
Upload BERT_Arch
e2e802f -
Upload config
9ca7d85 -
Upload BERT_Arch
f97a9bd -
Upload BERT_Arch
43207b3 -
Upload BERT_Arch
6ed4f80 -
Upload BERT_Arch
582e69b -
Upload BERT_Arch
f650316 -
Upload BERT_Arch
abe9f5e -
Upload BERT_Arch
ed321d4 -
Upload BERT_Arch
270001c -
Upload BERT_Arch
f051583 -
Upload BERT_Arch
71b6cec -
Upload tokenizer
e1f7d8f -
initial commit
f965821