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The pipeline tag "conversational" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, text2text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, 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-retrieval, time-series-forecasting, text-to-video, image-text-to-text, 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, other
⚠️ This model is deprecated. Please don't use it as it produces embeddings of low quality. We recommend using triple-encoders instead, also if you want to use them as a classic bi-encoder.
Imaginary Embeddings utilize Curved Contrastive Learning (see paper Imagination Is All You Need! (ACL 2023)) on Sentence Transformers for long-short term dialogue planning and efficient abstract sequence modeling.
This model uses speaker tokens and was evaluated in the Short-Term planning experiments.
Setup
python -m pip install imaginaryNLP
Usage
candidates = ['Want to eat something out ?',
'Want to go for a walk ?']
goal = ' I am hungry.'
stp.short_term_planning(candidates, goal)
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