Update README.md

README.md

@@ -15,4 +15,126 @@ pipeline_tag: text-generation
 metrics:
 - precision
 ---
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+
+# Model Card for Shorsey-T2000
+
+## Model Details
+
+### Model Description
+
+The Shorsey-T2000 is a custom hybrid model that combines the power of transformer-based architectures with recurrent neural networks (RNNs). Specifically, it integrates the self-attention mechanisms from Transformer-XL and T5 models with an LSTM layer to enhance the model's ability to handle complex sequence learning and long-range dependencies in text data. This model is versatile, designed to perform tasks such as text generation, causal language modeling, and question answering.
+
+- **Developed by:** Morgan Griffin, WongrifferousAI
+- **Funded by [optional]:** WongrifferousAI
+- **Shared by [optional]:** WongrifferousAI
+- **Model type:** Hybrid Transformer-RNN (TransformerXL-T5 with LSTM)
+- **Language(s) (NLP):** English (en)
+- **Finetuned from model [optional]:** Custom architecture
+
+### Direct Use
+
+This model can be used directly for:
+- **Text Generation:** Generating coherent and contextually relevant text sequences.
+- **Causal Language Modeling:** Predicting the next word in a sequence, which can be applied to various NLP tasks like auto-completion or story generation.
+- **Question Answering:** Providing answers to questions based on a given context.
+
+### Downstream Use [optional]
+
+The model can be fine-tuned for specific tasks such as:
+- **Sentiment Analysis:** Fine-tuning on datasets like IMDB for classifying sentiment in text.
+- **Summarization:** Adapting the model for generating concise summaries of longer text documents.
+
+### Out-of-Scope Use
+
+This model is not designed for:
+- **Real-time Conversational AI:** Due to the hybrid architecture and the complexity of the model, it may not be optimal for real-time, low-latency applications.
+- **Tasks requiring multilingual support:** The model is currently trained and optimized for English language processing only.
+
+## Bias, Risks, and Limitations
+
+As with any AI model, the Shorsey-T2000 may have biases present in the training data, which could manifest in its outputs. It's important to recognize:
+- **Bias in Training Data:** The model may reflect biases present in the datasets it was trained on, such as stereotypes or unbalanced representations of certain groups.
+- **Limited Context Understanding:** Despite the RNN integration, the model might struggle with highly nuanced context or very long-term dependencies beyond its training data.
+
+### Recommendations
+
+- **Human-in-the-Loop:** For applications where fairness and bias are critical, it's recommended to have a human review outputs generated by the model.
+- **Bias Mitigation:** Consider using additional data preprocessing techniques or post-processing steps to mitigate biases in the model's predictions.
+
+## How to Get Started with the Model
+
+You can start using the Shorsey-T2000 model with the following code snippet:
+
+```python
+from transformers import BertTokenizerFast, AutoModel
+
+tokenizer = BertTokenizerFast.from_pretrained("Wonder-Griffin/Shorsey-T2000")
+model = AutoModel.from_pretrained("Wonder-Griffin/Shorsey-T2000")
+
+input_text = "Once upon a time"
+input_ids = tokenizer(input_text, return_tensors="pt").input_ids
+
+# Generate text
+output = model.generate(input_ids, max_length=100)
+generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
+print(generated_text)
+
+##Training Data
+
+The model was trained on the stanfordnlp/imdb dataset, which contains movie reviews labeled with sentiment. Additional datasets may have been used for other tasks like question answering and language modeling.
+
+## Preprocessing [optional]
+
+Text data was tokenized using the standard transformer tokenizer, with additional preprocessing steps to ensure consistent input formatting across different tasks.
+## Training Hyperparameters
+
+    Training regime: fp32 precision, AdamW optimizer, learning rate of 3e-5, batch size of 8.
+    Max epochs: 10 epochs
+    Learning Rate Schedule: Linear decay with warmup steps.
+
+## Speeds, Sizes, Times [optional]
+
+    Training Time: Approximately 36 hours on a single NVIDIA V100 GPU.
+    Model Size: ~500M parameters
+    Checkpoint Size: ~2GB
+
+
+## Testing Data
+
+The model was tested on a held-out portion of the stanfordnlp/imdb dataset to evaluate its performance on sentiment classification and text generation tasks.
+Factors
+
+    Domain: Movie reviews, general text generation.
+    Subpopulations: Different sentiment categories (positive, negative).
+
+## Metrics
+
+    Precision: Used to evaluate the model's accuracy in generating correct text and answering questions.
+
+## Results
+
+The model demonstrated strong performance on text generation tasks, particularly in generating coherent and contextually appropriate responses. However, it shows a slight tendency towards generating overly positive or negative responses based on the context provided.
+Summary
+
+The Shorsey-T2000 is a versatile and powerful model for various NLP tasks, especially in text generation and language modeling. Its hybrid architecture makes it particularly effective in capturing both short-term and long-term dependencies in text.
+Technical Specifications [optional]
+Model Architecture and Objective
+
+The Shorsey-T2000 is a hybrid model combining Transformer-XL and T5 architectures with an LSTM layer to enhance sequence learning capabilities. It uses multi-head self-attention mechanisms, positional encodings, and RNN layers to process and generate text.
+
+## Model Card Authors [optional]
+
+    Morgan Griffin, WongrifferousAI
+
+## Model Card Contact
+
+    Contact: Morgan Griffin, WongrifferousAI
+
+
+### Summary of Key Information:
+- **Model Name:** Shorsey-T2000
+- **Model Type:** Hybrid Transformer-RNN (TransformerXL-T5 with LSTM)
+- **Developed by:** Morgan Griffin, WongrifferousAI
+- **Primary Tasks:** Text generation, causal language modeling, question answering
+- **Language:** English
+- **Key Metrics:** Precision, among others