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README.md

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-license: apache-2.0
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+---
+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