|
--- |
|
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 |
