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  It was trained on a large corpus of text, including some emotionally engaging datasets such as the "Facebook Empathetic Dialogues" dataset containing 25k conversations.
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  A dataset of 25k conversations grounded in emotional situations to facilitate training and evaluating dialogue systems.
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  ```python
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- from transformers import AutoTokenizer, AutoModelForCausalLM
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-
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- tokenizer = AutoTokenizer.from_pretrained("AliiaR/DialoGPT-medium-empathetic-dialogues")
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-
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- model = AutoModelForCausalLM.from_pretrained("AliiaR/DialoGPT-medium-empathetic-dialogues")
 
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  It was trained on a large corpus of text, including some emotionally engaging datasets such as the "Facebook Empathetic Dialogues" dataset containing 25k conversations.
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  A dataset of 25k conversations grounded in emotional situations to facilitate training and evaluating dialogue systems.
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  ```python
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+ >>> from transformers import AutoTokenizer, AutoModelForCausalLM
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+ >>> tokenizer = AutoTokenizer.from_pretrained("AliiaR/DialoGPT-medium-empathetic-dialogues")
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+ >>> model = AutoModelForCausalLM.from_pretrained("AliiaR/DialoGPT-medium-empathetic-dialogues")
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+ ```