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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, 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
About model
A customer support chatbot built on Google's T5 architecture and fine-tuned using the bitext/Bitext-customer-support-llm-chatbot-training-dataset. Designed to understand natural language and provide accurate, efficient responses for a wide range of customer service scenarios. Ideal for automating support, answering queries, and enhancing user experience in customer-facing applications.
How to Get Started with the Model
from transformers import T5Tokenizer, T5ForConditionalGeneration
model_name = "sunbv56/T5_Chatbot_CustomerSupport"
tokenizer = T5Tokenizer.from_pretrained(model_name, legacy=False)
model = T5ForConditionalGeneration.from_pretrained(model_name)
Example code here
https://www.kaggle.com/code/thuntrngbnh/test-t5-chatbot-customersupport
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