YAML Metadata Warning:The pipeline tag "text2text-generation" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other
Model was trained on companion dataset. Minibob guess word from a description modeling well known Alias word game.
from transformers import T5ForConditionalGeneration, T5Tokenizer
prefix = "guess word:"
def predict_word(prompt, model, tokenizer):
prompt = prompt.replace("...", "<extra_id_0>")
prompt = f"{prefix} {prompt}"
input_ids = tokenizer([prompt], return_tensors="pt").input_ids
outputs = model.generate(
input_ids.to(model.device),
num_beams=5,
max_new_tokens=8,
do_sample=False,
num_return_sequences=5
)
candidates = set()
for tokens in outputs:
candidate = tokenizer.decode(tokens, skip_special_tokens=True)
candidate = candidate.strip().lower()
candidates.add(candidate)
return candidates
model_name = "artemsnegirev/minibob"
tokenizer = T5Tokenizer.from_pretrained(model_name)
model = T5ForConditionalGeneration.from_pretrained(model_name)
prompt = "это животное с копытами на нем ездят"
print(predict_word(prompt, model, tokenizer))
# {'верблюд', 'конь', 'коня', 'лошадь', 'пони'}
Detailed github-based tutorial with pipeline and source code for building Minibob
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