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:
# 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
File size: 463 Bytes
98c533d | 1 2 3 4 5 6 7 8 9 10 11 12 13 | # 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 |