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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, other
Model description
DialoGPT finetuned on empathetic dialogues
Training data
It was trained on a large corpus of text, including some emotionally engaging datasets such as the "Facebook Empathetic Dialogues" dataset containing 25k conversations. A dataset of 25k conversations grounded in emotional situations to facilitate training and evaluating dialogue systems. You can find a dataset here.
How to use
>>> from transformers import AutoTokenizer, AutoModelForCausalLM
>>> tokenizer = AutoTokenizer.from_pretrained("AliiaR/DialoGPT-medium-empathetic-dialogues")
>>> model = AutoModelForCausalLM.from_pretrained("AliiaR/DialoGPT-medium-empathetic-dialogues")
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