This is the checkpoint for T5-large after being trained on the SemCor 3.0 dataset.
Additional information about this model:
- The t5-large model page
- Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
- Official implementation by Google
The model can be loaded to perform a few-shot classification like so:
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer AutoModelForSeq2SeqLM.from_pretrained("jpelhaw/t5-word-sense-disambiguation") AutoTokenizer.from_pretrained("jpelhaw/t5-word-sense-disambiguation") input = 'question: which description describes the word " java " best in the following context? \ descriptions:[ " A drink consisting of an infusion of ground coffee beans " , " a platform-independent programming lanugage " , or " an island in Indonesia to the south of Borneo " ] context: I like to drink " java " in the morning .' example = tokenizer.tokenize(input, add_special_tokens=True) answer = model.generate(input_ids=example['input_ids'], attention_mask=example['attention_mask'], max_length=135) # "a distinguishing trait"
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