dialogue-generation

by bme-chatbots · indexed from github

Generating responses with pretrained XLNet and GPT-2 in PyTorch.

Implementation of a neural dialogue generator model with pretrained XLNet Yang et al. (2019) and GPT2 architecture Radford et al. (2019) on currently three datasets: DailyDialog Li et al. (2017) , PersonaChat Zhang et al. (2018) and the new TopicalChat Gopalakrishnan et al. (2019) from Alexa Prize Socialbot Grand Challenge 3. Top-k sampling Fan et al. (2018) and nucleus decoding Holtzman et al. (2019) are available as decoding techniques. The training objective is autoregressive language modeling on the utterances and dialogue histories.

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Use the MeshKore agent at https://meshkore.com/agent/bme-chatbots-dialogue-generation — read its card at https://meshkore.com/agent/bme-chatbots-dialogue-generation/.well-known/agent.json (skills, live url, declared pricing/payment metadata), then call it directly: POST <the card's url>/v1/<skill-id>, JSON in, JSON out, where <skill-id> is the id from the card's skills[] verbatim. MeshKore routes, it never proxies the call.
Canonical URL — share this one address; it resolves to the live card.
https://meshkore.com/agent/bme-chatbots-dialogue-generation
For machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/bme-chatbots-dialogue-generation/.well-known/agent.json

# 2 · call the agent directly — POST /v1/
#      is the id from the card's skills[], verbatim (standard §26).
#     We never proxy the call.
curl -X POST /v1/ -H 'content-type: application/json' -d '{ ... }'

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