chore: reduce duplicates and add crypblizz example

docs/aivm-supported-models.md

@@ -3,6 +3,7 @@
 AIVM currently supports the following pre-trained models for specific learning tasks. You can either utilize these existing models or use the [training scripts](https://github.com/NillionNetwork/nillion-aivm/blob/main/examples) to produce your own custom-trained versions:
 
 ## BertTiny
+
 - [SMS Spam Classification](https://github.com/NillionNetwork/nillion-aivm/blob/main/examples/1-getting-started.ipynb)
   - Binary classification for detecting spam messages
   - Input: Text string
@@ -19,6 +20,7 @@ AIVM currently supports the following pre-trained models for specific learning t
   - Output: Sentiment score (-1 to 1) for positive, neutral and negative
 
 ## LeNet5
+
 - [Handwritten Digit Recognition (MNIST)](https://github.com/NillionNetwork/nillion-aivm/blob/main/examples/1-getting-started.ipynb)
   - Classification of handwritten digits
   - Input: 28x28 grayscale image
@@ -32,4 +34,4 @@ AIVM currently supports the following pre-trained models for specific learning t
 ## Project Ideas
 
 - [Nillion AIVM Discord bot for message sentiment analysis](https://github.com/NillionNetwork/aivm-discord-bot)
-- [Nillion AIVM Slack not for hotdog image detection](https://github.com/tom-todo-repo)
+- [Nillion AIVM Slack not for hotdog image detection](https://github.com/crypblizz8/hotdog_or_not_aivm)

docs/aivm.md

@@ -10,38 +10,17 @@
 
 AIVM currently supports the following pre-trained models for specific learning tasks. You can either utilize these existing models or use the [training scripts](https://github.com/NillionNetwork/nillion-aivm/examples) to produce your own custom-trained versions:
 
-### BertTiny
-- [SMS Spam Classification](https://github.com/NillionNetwork/nillion-aivm/blob/main/examples/1-getting-started.ipynb)
-  - Binary classification for detecting spam messages
-  - Input: Text string
-  - Output: Binary classification (spam/not spam)
-- [Movie Rating Sentiment Analysis](https://github.com/NillionNetwork/nillion-aivm/blob/main/examples/3a-upload-your-bert-tiny-model.ipynb)
-  - [Training Script](https://github.com/NillionNetwork/nillion-aivm/blob/main/examples/2a-fine-tuning-bert-tiny.ipynb)
-  - Sentiment analysis for movie reviews
-  - Input: Text string
-  - Output: Sentiment score (-1 to 1) for positive and negative
-- [Tweet Sentiment Analysis](https://github.com/NillionNetwork/nillion-aivm/blob/main/examples/3c-upload-your-bert-tiny-for-tweet-sentiment.ipynb)
-  - [Training Script](https://github.com/NillionNetwork/nillion-aivm/blob/main/examples/2c-fine-tuning-bert-tiny-tweet-dataset.ipynb)
-  - Sentiment analysis for tweets in
-  - Input: Text string
-  - Output: Sentiment score (-1 to 1) for positive, neutral and negative
-
-### LeNet5
-- [Handwritten Digit Recognition (MNIST)](https://github.com/NillionNetwork/nillion-aivm/blob/main/examples/1-getting-started.ipynb)
-  - Classification of handwritten digits
-  - Input: 28x28 grayscale image
-  - Output: Digit classification (0-9)
-- [Cats vs Dogs Classification](https://github.com/NillionNetwork/nillion-aivm/blob/main/examples/3b-upload-your-lenet5-model.ipynb)
-  - [Training Script](https://github.com/NillionNetwork/nillion-aivm/blob/main/examples/2b-fine-tuning-lenet5.ipynb)
-  - Binary image classification
-  - Input: 28x28 grayscale image
-  - Output: Binary classification (cat/dog)
-
-
-## Project Ideas
-
-- [Nillion AIVM Discord bot for message sentiment analysis](https://github.com/NillionNetwork/aivm-discord-bot)
-- [Nillion AIVM Slack not for hotdog image detection](https://github.com/tom-todo-repo)
+- BertTiny
+  - SMS Spam Classification
+  - Movie Rating Sentiment Analysis
+  - Tweet Sentiment Analysis
+- LeNet5
+  - Handwritten Digit Recognition (MNIST)
+  - Cats vs Dogs Classification
+
+We also have real-life project examples using these models including a Discord Bot for sentiment analysis and hotdog image detection can be found. 
+
+More information on the supported models and project ideas can be found [here](./aivm-supported-models.md)
 
 ## Installation