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example_title: "Question Answering"
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- text: "Q: Can Geoffrey Hinton have a conversation with George Washington? Give the rationale before answering."
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example_title: "Logical reasoning"
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- text: "Please answer the following question. What is the boiling point of Nitrogen?"
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example_title: "Scientific knowledge"
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- text: "Answer the following yes/no question. Can you write a whole Haiku in a single tweet?"
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example_title: "Yes/no question"
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- text: "Answer the following yes/no question by reasoning step-by-step. Can you write a whole Haiku in a single tweet?"
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example_title: "Reasoning task"
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- text: "Q: ( False or not False or False ) is? A: Let's think step by step"
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example_title: "Boolean Expressions"
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- text: "The square root of x is the cube root of y. What is y to the power of 2, if x = 4?"
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example_title: "Math reasoning"
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- text: "Premise: At my age you will probably have learnt one lesson. Hypothesis: It's not certain how many lessons you'll learn by your thirties. Does the premise entail the hypothesis?"
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example_title: "Premise and hypothesis"
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- text: "Hey my name is Thomas! How are you?"
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example_title: "Chatting"
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metrics:
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- accuracy
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- f1
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Using an AIML Chatbot will allow you to hardcode some replies also.
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It's main target platform is discord.
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You can invite the bot [here](https://aeona.xyz).
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The main reason for using google flan is its great performance at logical reasoning.
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# Participate and Help the AI improve or just hang out at [hugging face discussions](https://huggingface.co/deepparag/Aeona/discussions)
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tags:
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- generated_from_trainer
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model-index:
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- name: Aeona-Beta-New
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results: []
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# Aeona-Beta-New
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This model is a fine-tuned version of [deepparag/Aeona-Beta-New](https://huggingface.co/deepparag/Aeona-Beta-New) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 3.5186
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 5e-05
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- train_batch_size: 9
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- eval_batch_size: 8
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- seed: 42
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- num_epochs: 1
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### Training results
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| Training Loss | Epoch | Step | Validation Loss |
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|:-------------:|:-----:|:----:|:---------------:|
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| 3.6586 | 1.0 | 8642 | 3.5186 |
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### Framework versions
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- Transformers 4.26.0
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- Pytorch 1.11.0
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- Datasets 2.1.0
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- Tokenizers 0.12.1
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