Update README.md

README.md

@@ -9,23 +9,23 @@ metrics:
 - perplexity
 - loss
 ---
-#Model Card
-##Model Details
+# Model Card
+## Model Details
 Model Name: gpt2-conversational-or-qa
 Model Type: Language Modeling
 Task: Generating Conversational Responses
 Description: This model is trained on a dataset of conversations between a user and an AI assistant, with the goal of generating a coherent and relevant response to the user's input. It uses the GPT-2 architecture, a state-of-the-art transformer-based language model that is capable of generating high-quality text with a wide range of styles and tones. The model is fine-tuned on the conversational data using maximum likelihood estimation, and is evaluated based on its ability to generate responses that are both grammatically correct and semantically relevant to the user's input.
-##Intended Use
+## Intended Use
 This model is intended to be used for generating conversational responses in a variety of contexts, such as chatbots, virtual assistants, and customer service applications. It is designed to provide natural and engaging responses to user input, with a focus on maintaining a consistent tone and style throughout the conversation. The model is suitable for use in both text-based and voice-based interfaces, and can be easily integrated into existing applications using the PyTorch and Transformers frameworks.
 
-##Training Data
+## Training Data
 The model is trained on a large dataset of conversational data, consisting of interactions between users and an AI assistant. The data is preprocessed to remove any sensitive information and is formatted in a way that is suitable for training a language model. The training data is split into a training set and a validation set, with the training set used to update the model parameters and the validation set used to evaluate the model performance.
 
-##Model Architecture
+## Model Architecture
 The model architecture used in this model is GPT-2, a transformer-based language model that is capable of generating high-quality text with a wide range of styles and tones. The GPT-2 architecture consists of a multi-layered transformer encoder-decoder, with self-attention mechanisms that allow the model to capture long-term dependencies and generate coherent text.
 
-##Evaluation Metrics
+## Evaluation Metrics
 The model is evaluated based on several metrics, including loss, reward, BLEU score, and perplexity. The loss metric is calculated during training and reflects the difference between the predicted output and the actual output. The reward metric is based on the number of correct words generated by the model, while the penalty metric penalizes the model for repeating words consecutively. The BLEU score measures the similarity between the generated text and the ground truth text, while the perplexity metric measures how well the model is able to predict the next word in a sequence.
 
-##Limitations and Bias
+## Limitations and Bias
 One limitation of this model is that it may generate responses that are biased or inappropriate, depending on the nature of the training data. Care should be taken to ensure that the training data is diverse and representative of the target user population, in order to minimize the risk of bias or discrimination. Additionally, the model may struggle with generating responses that are highly domain-specific or technical in nature, as these may require specialized knowledge or context that is not present in the training data.