Update README.md

README.md

@@ -4,4 +4,52 @@ datasets:
 - BEE-spoke-data/fineweb-cryptid-5k
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-New model tuning stratagy. Adding text to make this long enough.
+New model tuning stratagy. Adding text to make this long enough.
+
+Model Description
+
+The Cryptid Detection Model is designed to generate and classify text related to cryptids, which are creatures from folklore and urban legends. The model is trained on a diverse dataset of cryptid-related content, including descriptions, stories, sightings, and various folklore sources.
+Intended Use
+
+    Primary Use Case: Generating and classifying text about cryptids for entertainment, research, and educational purposes.
+    Secondary Use Cases: Assisting in the creation of cryptid-related content for books, articles, and media.
+
+Input and Output
+
+    Input: Text prompts or descriptions.
+    Output: Generated text about cryptids or classifications of the input text as related to specific cryptids.
+
+Training Data
+
+The model was trained on a curated dataset of cryptid-related text, including but not limited to:
+
+    Books and articles about cryptids.
+    Online forums and discussion boards.
+    Folklore databases.
+    User-submitted stories and sightings.
+
+Data Preprocessing
+
+    Text cleaning: Removal of special characters, HTML tags, and excessive whitespace.
+    Tokenization: Breaking down text into tokens for training.
+
+Model Performance
+
+    Metrics: [Accuracy, F1 Score, Precision, Recall, etc.]
+    Evaluation: The model was evaluated on a validation set consisting of [describe the validation set].
+
+Limitations and Biases
+
+    Biases: The model may reflect biases present in the training data, such as regional biases in folklore or common myths.
+    Limitations: The model may not accurately generate or classify less common or very specific cryptids.
+
+Ethical Considerations
+
+    The model is intended for entertainment and educational purposes. It should not be used as a factual source for scientific research or investigation.
+    Users should be aware of the potential for generating content that might be misinterpreted as factual.
+
+Future Work
+
+    Expanding the training dataset to include more diverse sources.
+    Improving classification accuracy for less common cryptids.
+    Adding functionality for multilingual support.