Transformers
JAX
Safetensors
English
t5
text2text-generation
biomedical
clinical
ul2
encoder-decoder
pretraining
medical
text-generation-inference
Instructions to use Siddharth63/medul2-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Siddharth63/medul2-base with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Siddharth63/medul2-base") model = AutoModelForSeq2SeqLM.from_pretrained("Siddharth63/medul2-base") - Notebooks
- Google Colab
- Kaggle
| # T5.1.1 Efficient base nl36 model. | |
| import seqio | |
| include 't5x/examples/t5/t5_1_1/base.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 |