Instructions to use allenai/PRIMERA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use allenai/PRIMERA with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("allenai/PRIMERA") model = AutoModelForSeq2SeqLM.from_pretrained("allenai/PRIMERA", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
-
Upload README.md with huggingface_hub
0a39645
-
Add trigram block by default
Similar to PRIMERA-multinews and PRIMERA-multixscience, I have found that I get ROUGE scores much closer to those reported if I add a trigram block. I am proposing this be enabled by default in the HuggingFace config.bd01102
-
Add TF weights (#1)
joaogante- Add TF weights (6fc49b6c4743cc74bdb263fdf92c81c484237665)550385a
-
update config.json
update max_position_embeddings as 4096, which is used in `run_summarization.py` in the huggingface library.0495403
-
Update README.md
1d3afa9 -
Update README.md
cb83f18 -
Upload vocab.json
ddb8cda -
Upload tokenizer_config.json
44fe30d -
Upload special_tokens_map.json
280d283
-
Upload pytorch_model.bin with git-lfs
9de6d23 -
Upload merges.txt
873efb4 -
Upload added_tokens.json
6300df6 -
Upload config.json
2d10135 -
Update README.md
10f9b14 -
Update README.md
53d1257 -
initial commit
832e6be