Instructions to use allenai/specter with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use allenai/specter with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="allenai/specter")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("allenai/specter") model = AutoModel.from_pretrained("allenai/specter") - Inference
- Notebooks
- Google Colab
- Kaggle
Add TF weights
#3
by joaogante - opened
Model converted by the transformers' pt_to_tf CLI.
All converted model outputs and hidden layers were validated against its Pytorch counterpart. Maximum crossload output difference=1.335e-05; Maximum converted output difference=1.335e-05.
cc @patrickvonplaten [HF maintainer(s) for this repo]
(I see there is another TF weights PR, although there is a small mismatch on the weights. The difference could be explained by the position ids, which don't need to be loaded.)
Ok to merge for me - thanks @joaogante
joaogante changed pull request status to merged
I'll close my PR then.