| language: en | |
| Next word generator trained on questions. Receives partial questions and tries to predict the next word. | |
| Example use: | |
| ```python | |
| from transformers import T5Config, T5ForConditionalGeneration, T5Tokenizer | |
| model_name = "allenai/t5-small-squad2-next-word-generator-squad" | |
| tokenizer = T5Tokenizer.from_pretrained(model_name) | |
| model = T5ForConditionalGeneration.from_pretrained(model_name) | |
| def run_model(input_string, **generator_args): | |
| input_ids = tokenizer.encode(input_string, return_tensors="pt") | |
| res = model.generate(input_ids, **generator_args) | |
| output = tokenizer.batch_decode(res, skip_special_tokens=True) | |
| print(output) | |
| return output | |
| run_model("Which") | |
| run_model("Which two") | |
| run_model("Which two counties") | |
| run_model("Which two counties are") | |
| run_model("Which two counties are the") | |
| run_model("Which two counties are the biggest") | |
| run_model("Which two counties are the biggest economic") | |
| run_model("Which two counties are the biggest economic powers") | |
| ``` | |
| which should result in the following: | |
| ``` | |
| ['one'] | |
| ['statements'] | |
| ['are'] | |
| ['in'] | |
| ['most'] | |
| ['in'] | |
| ['zones'] | |
| ['of'] | |
| ``` | |