Transformers
PyTorch
JAX
English
t5
text2text-generation
biomedical
clinical
ul2
encoder-decoder
pretraining
medical
text-generation-inference
Instructions to use Siddharth63/pubmedul2_small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Siddharth63/pubmedul2_small with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Siddharth63/pubmedul2_small") model = AutoModelForSeq2SeqLM.from_pretrained("Siddharth63/pubmedul2_small", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download small.gin from Siddharth63/pubmedul2_small: direct link, hf CLI and curl.
- Browser
- Download file 463 Bytes
-
https://huggingface.co/Siddharth63/pubmedul2_small/resolve/main/small.gin
- Command line
-
hf download hf://Siddharth63/pubmedul2_small/small.gin
-
curl -L -o small.gin https://huggingface.co/Siddharth63/pubmedul2_small/resolve/main/small.gin
463 Bytes
| # T5.1.1 Efficient base nl36 model. | |
| import seqio | |
| include 't5x/examples/t5/t5_1_1/small.gin' # imports vocab, optimizer and model. | |
| # ------------------- Model specification overrides -------------------------- | |
| VOCABULARY = @seqio.SentencePieceVocabulary() | |
| seqio.SentencePieceVocabulary.sentencepiece_model_file = "spiece.model" | |
| MODEL = @models.EncoderDecoderModel() | |
| models.EncoderDecoderModel: | |
| input_vocabulary = %VOCABULARY | |
| output_vocabulary = %VOCABULARY |