dnnsdunca / agentic-Transformer

license: mit language:

• en metrics:
• accuracy library_name: adapter-transformers tags:
• code Here is a sample model card for the project: Model Card: Multitask Learning for Agent-Action Identification Model Name: Agent-Action Identifier Model Type: Multitask Learning Model Model Description: The Agent-Action Identifier is a multitask learning model that identifies agents and actions in text data. The model is trained on a custom dataset of text examples, where each example is annotated with the agents and actions present in the text. Model Architecture: Encoder: BERT (bert-base-uncased) Classification Heads: Two classification heads for agents and actions Model Parameters: 120M parameters Training Data: Dataset: Custom dataset of text examples Training Set: 10,000 examples Validation Set: 1,250 examples Testing Set: 1,250 examples Training Hyperparameters: Batch Size: 16 Number of Epochs: 3 Learning Rate: 1e-5 Optimizer: AdamW Evaluation Metrics: Accuracy: 92.5% on validation set F1-Score: 91.2% on validation set Intended Use: The Agent-Action Identifier is intended for use in natural language processing applications, such as text analysis and information extraction. Limitations: Dataset bias: The model is trained on a custom dataset and may not generalize well to other datasets. Overfitting: The model may overfit to the training data, especially if the training set is small. Ethics: Data privacy: The dataset used to train the model is anonymized and does not contain any personally identifiable information. Bias and fairness: The model is designed to be fair and unbiased, but may still reflect biases present in the training data. Model Performance: Accuracy: 92.5% on validation set F1-Score: 91.2% on validation set Precision: 93.1% on validation set Recall: 91.5% on validation set How to Use: Input: Text data Output: Identified agents and actions Code: Python code using the Hugging Face Transformers library Citation: If you use the Agent-Action Identifier in your research, please cite the following paper: [Insert paper citation] License: The Agent-Action Identifier is licensed under the MIT License. Contact: For more information, please contact [ [email protected] ]. I hope this sample model card meets your requirements! Let me know if you have any further requests. Generated by Meta Llama 3.1-405B