update model card README.md

README.md

@@ -1,71 +1,55 @@
 ---
-pipeline_tag: text2text-generation
-widget:
-- text: "Translate to German:  My name is Arthur"
-  example_title: "Translation"
-- text: "Please answer to the following question. Who is going to be the next Ballon d'or?"
-  example_title: "Question Answering"
-- text: "Q: Can Geoffrey Hinton have a conversation with George Washington? Give the rationale before answering."
-  example_title: "Logical reasoning"
-- text: "Please answer the following question. What is the boiling point of Nitrogen?"
-  example_title: "Scientific knowledge"
-- text: "Answer the following yes/no question. Can you write a whole Haiku in a single tweet?"
-  example_title: "Yes/no question"
-- text: "Answer the following yes/no question by reasoning step-by-step. Can you write a whole Haiku in a single tweet?"
-  example_title: "Reasoning task"
-- text: "Q: ( False or not False or False ) is? A: Let's think step by step"
-  example_title: "Boolean Expressions"
-- text: "The square root of x is the cube root of y. What is y to the power of 2, if x = 4?"
-  example_title: "Math reasoning"
-- 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?"
-  example_title: "Premise and hypothesis"
-- text: "Hey my name is Thomas! How are you?"
-  example_title: "Chatting"
-metrics:
-- accuracy
-- f1
+tags:
+- generated_from_trainer
+model-index:
+- name: Aeona-Beta-New
+  results: []
 ---
 
-# Aeona
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
 
-<!-- Provide a quick summary of what the model is/does. -->
+# Aeona-Beta-New
 
+This model is a fine-tuned version of [deepparag/Aeona-Beta-New](https://huggingface.co/deepparag/Aeona-Beta-New) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 3.5186
 
-# Model Details
+## Model description
 
-An generative AI based on [Google Flan Base](https://huggingface.co/google/flan-t5-base).
+More information needed
 
-Recommended to use along with an [AIML Chatbot](https://github.com/deepsarda/Aeona-Aiml) to reduce load, get better replies, add name and personality to your bot.
-Using an AIML Chatbot will allow you to hardcode some replies also.
+## Intended uses & limitations
 
-## Model Description
+More information needed
 
-eona is an chatbot which hope's to be able to talk with humans as if its an friend!
-It's main target platform is discord. 
-You can invite the bot [here](https://aeona.xyz).
+## Training and evaluation data
 
-To learn more about this project and chat with the ai, you can use this [website](https://aeona.xyx/).
+More information needed
 
-Aeona works by sending a query to the AIML Chatbot if the AIML chabot is unable to answer then it comes over here to ***apply some real brains*** so to speak.
-The main reason for using google flan is its great performance at logical reasoning.
-# Participate and Help the AI improve or just hang out at [hugging face discussions](https://huggingface.co/deepparag/Aeona/discussions)
+## Training procedure
 
-## Goals
- The goal is to create an AI which will work with AIML in order to create the most human like AI.
- 
- #### Why not an AI on its own?
- For AI it is not possible (realistically) to learn about the user and store data on them, when compared to an AIML which can even execute code!
- The goal of the AI is to generate responses where the AIML fails.
- 
- Hence the goals becomes to make an AI which has a wide variety of knowledge, yet be as small as possible!
- So we use 3 dataset:-
- 1. [Movielines](https://www.kaggle.com/Cornell-University/movie-dialog-corpus) The movie lines promote longer and more thought out responses but it can be very random. About 200k lines!
- 2. [Discord Messages](https://www.kaggle.com/jef1056/discord-data) The messages are on a wide variety of topics filtered and removed spam which makes the AI highly random but gives it a very random response to every days questions! about 120 million messages!
- 3. Custom dataset scrapped from my messages, These messages are very narrow teaching this dataset and sending a random reply will make the AI say sorry loads of time!
-    
-## Training
- The Discord Messages Dataset simply dwarfs the other datasets, Hence the data sets are repeated.
- This leads to them covering each others issues!
-  
- The AI has a context of 6 messages which means it will reply until the 4th message from user.
- [Example](https://huggingface.co/deepparag/Aeona-Beta/discussions/1)
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 5e-05
+- train_batch_size: 9
+- eval_batch_size: 8
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 1
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss |
+|:-------------:|:-----:|:----:|:---------------:|
+| 3.6586        | 1.0   | 8642 | 3.5186          |
+
+
+### Framework versions
+
+- Transformers 4.26.0
+- Pytorch 1.11.0
+- Datasets 2.1.0
+- Tokenizers 0.12.1