Model Card for my-tf-nn-model-v18
This model is a TensorFlow neural network model trained using the Keras API. It has been uploaded to Hugging Face for sharing and potential deployment. The model is intended for classification tasks and has been optimized for accuracy and efficiency.
Model Details
Model Description
- Developed by: Kamal18
- Funded by: Self-funded
- Shared by: Kamal18
- Model type: Neural Network (Keras/TensorFlow)
- Language(s) (NLP, if applicable): Not applicable
- License: Apache 2.0
- Finetuned from model: Custom-trained model
Model Sources
- Repository: Hugging Face Model Page
- Paper [optional]: Not available
- Demo [optional]: Not available
Uses
Direct Use
This model is designed for supervised learning tasks, specifically classification problems. Users can load the model and use it for predictions on structured or unstructured datasets.
Downstream Use
The model can be fine-tuned on specific datasets to improve performance for domain-specific applications, such as image recognition or financial predictions.
Out-of-Scope Use
The model is not designed for real-time inference in resource-constrained environments, nor for applications requiring high interpretability, such as medical diagnosis without expert oversight.
Bias, Risks, and Limitations
Risks
- The model may have biases inherited from the training data.
- Performance may degrade if used on data distributions significantly different from the training data.
Recommendations
Users should evaluate the model on their specific use case and fine-tune it if necessary. It is also recommended to check for biases in predictions before deploying the model in production.
How to Get Started with the Model
1from transformers import AutoModel
2model = AutoModel.from_pretrained("KAMAL18/my-tf-nn-model-v18")
Training Details
Training Data
The model was trained on a custom dataset. The dataset was preprocessed to remove noise and standardize input features.
Training Procedure
- Preprocessing: Standardization and normalization of input features.
- Training Regime: FP32 precision.
- Optimizer: Adam optimizer.
- Loss Function: Categorical Crossentropy.
- Batch Size: 32.
- Epochs: 50.
- Validation Split: 20%.
Speeds, Sizes, Times
- Training Time: ~3 hours on a single GPU.
- Model Size: ~50MB.
Evaluation
Testing Data, Factors & Metrics
Testing Data
The model was evaluated on a separate test dataset consisting of 10,000 samples.
Factors
- Model was tested across different input variations to assess generalization performance.
Metrics
- Accuracy: 92%
- Precision: 91%
- Recall: 90%
- F1-score: 90.5%
Results
The model achieved state-of-the-art performance on the given dataset but may require fine-tuning for deployment in other domains.
Model Examination
Explainability techniques such as SHAP or LIME can be used to analyze feature importance in predictions.
Environmental Impact
- Hardware Type: NVIDIA RTX 3090 GPU
- Hours used: ~3 hours
- Cloud Provider: Local machine
- Compute Region: Not applicable
- Carbon Emitted: Estimated ~3 kg CO2
Technical Specifications
Model Architecture and Objective
- Neural Network with multiple dense layers.
- Activation functions: ReLU, Softmax.
- Output Layer: Softmax for classification.
Compute Infrastructure
- Hardware: NVIDIA RTX 3090 GPU
- Software: TensorFlow 2.x, Keras API
Citation
1@misc{Kamal18_tf_nn_model,
2 author = {Kamal18},
3 title = {TensorFlow Neural Network Model},
4 year = {2025},
5 publisher = {Hugging Face},
6 journal = {Hugging Face Model Hub},
7 url = {https://huggingface.co/KAMAL18/my-tf-nn-model-v18}
8}
More Information
For further details, visit the
Hugging Face model page or contact the author.
Model Card Authors
Model Card Contact
For questions or issues, reach out via the Hugging Face discussions section on the model page.
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"model_type": "bert",
"hidden_size": 768,
"num_attention_heads": 12,
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