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--- |
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language: |
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- en |
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widget: |
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- text: "Generate a dialogue between two people about the following topic: A local street market bustles with activity, #Person1# tries exotic food for the first time, and #Person2#, familiar with the cuisine, offers insights and recommendations. Dialogue:" |
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example_title: "Street Market" |
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- text: "Generate a dialogue between two people about the following topic: At a quiet park, #Person1# stumbles upon an eerie crime scene, and #Person2#, a detective, arrives and begins to unravel the mysterious circumstances of the murder. Dialogue:" |
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example_title: "Crime Scene" |
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tags: |
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- dialogue |
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- conversation-generator |
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- flan-t5-base |
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- fine-tuned |
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license: apache-2.0 |
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datasets: |
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- kaggle-dialogue-dataset |
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--- |
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# Omaratef3221/flan-t5-base-dialogue-generator |
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## Model Description |
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This model is a fine-tuned version of Google's `t5` specifically tailored for generating realistic and engaging dialogues or conversations. |
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It has been trained to capture the nuances of human conversation, making it highly effective for applications requiring conversational AI capabilities. |
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## Intended Use |
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`Omaratef3221/flan-t5-base-dialogue-generator` is ideal for developing chatbots, virtual assistants, and other applications where generating human-like dialogue is crucial. |
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It can also be used for research and development in natural language understanding and generation. |
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## How to Use |
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You can use this model directly with the transformers library as follows: |
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### Download the model |
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```python |
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model_name = "Omaratef3221/flan-t5-base-dialogue-generator" |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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model = AutoModelForSeq2SeqLM.from_pretrained(model_name) |
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``` |
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### Use with example |
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```python |
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from transformers import AutoModelForSeq2SeqLM, AutoTokenizer |
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model_name = "Omaratef3221/flan-t5-base-dialogue-generator" |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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model = AutoModelForSeq2SeqLM.from_pretrained(model_name) |
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prompt = ''' |
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Generate a dialogue between two people about the following topic: |
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A local street market bustles with activity, #Person1# tries exotic food for the first time, and #Person2#, familiar with the cuisine, offers insights and recommendations. |
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Dialogue: |
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''' |
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# Generate a response to an input statement |
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input_ids = tokenizer(prompt, return_tensors='pt').input_ids |
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output = model.generate(input_ids, top_p = 0.6, do_sample=True, temperature = 1.2, max_length = 512) |
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print(tokenizer.decode(output[0], skip_special_tokens=True).replace('. ', '.\n')) |
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``` |
