| --- |
| license: mit |
| language: |
| - en |
| library_name: adapter-transformers |
| tags: |
| - code |
| --- |
| --- |
| 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 [dduncan@ddroidlabs.com]. |
| 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 |
