goatley
/

sentiment-final-model

@@ -3,197 +3,161 @@ library_name: transformers
 license: mit
 ---
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
 ## Model Details
 ### Model Description
-<!-- Provide a longer summary of what this model is. -->
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-- **Developed by:** [Keith Goatley]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [MIT]
-- **Finetuned from model [optional]:** [More Information Needed]
-### Model Sources [optional]
-<!-- Provide the basic links for the model. -->
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
 ## Uses
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
 ### Direct Use
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-[More Information Needed]
-### Downstream Use [optional]
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-[More Information Needed]
 ### Out-of-Scope Use
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-[More Information Needed]
 ## Bias, Risks, and Limitations
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-[More Information Needed]
 ### Recommendations
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
 ## How to Get Started with the Model
-Use the code below to get started with the model.
-[More Information Needed]
-## Training Details
-### Training Data
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-[More Information Needed]
-### Training Procedure
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-#### Preprocessing [optional]
-[More Information Needed]
-#### Training Hyperparameters
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-#### Speeds, Sizes, Times [optional]
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-[More Information Needed]
-## Evaluation
-<!-- This section describes the evaluation protocols and provides the results. -->
-### Testing Data, Factors & Metrics
-#### Testing Data
-<!-- This should link to a Dataset Card if possible. -->
-[More Information Needed]
-#### Factors
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-[More Information Needed]
-#### Metrics
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-[More Information Needed]
-### Results
-[More Information Needed]
-#### Summary
-## Model Examination [optional]
-<!-- Relevant interpretability work for the model goes here -->
-[More Information Needed]
-## Environmental Impact
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-## Technical Specifications [optional]
-### Model Architecture and Objective
-[More Information Needed]
-### Compute Infrastructure
-[More Information Needed]
-#### Hardware
-[More Information Needed]
-#### Software
-[More Information Needed]
-## Citation [optional]
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-**BibTeX:**
-[More Information Needed]
-**APA:**
-[More Information Needed]
-## Glossary [optional]
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-[More Information Needed]
-## More Information [optional]
-[More Information Needed]
-## Model Card Authors [optional]
-[More Information Needed]
-## Model Card Contact
-[More Information Needed]

 license: mit
 ---
+# Model Card for `goatley/sentiment-final-model`
+This model is a fine-tuned **DistilBERT** model for **binary sentiment classification** (positive/negative) of English text reviews.
+It was developed as part of an advanced NLP dashboard project demonstrating applied skills in deep learning, NLP engineering, and full-stack app deployment.
 ## Model Details
 ### Model Description
+- **Developed by:** Keith Goatley
+- **License:** MIT
+- **Model type:** DistilBERT-based Sequence Classification (Binary)
+- **Language(s):** English
+- **Fine-tuned from:** `distilbert-base-uncased`
+- **Base model:** Hugging Face Transformers v4
+- **Framework:** PyTorch
+### Model Sources
+- **Repository:** [GitHub Repository](https://github.com/Keithgoatley/sentiment-analysis-app)
+- **Demo:** [Hugging Face Space (when deployed)](https://huggingface.co/spaces/goatley/sentiment-analysis-dashboard)
 ## Uses
 ### Direct Use
+- Classifying short text reviews (e.g., Amazon product reviews) into **positive** or **negative** sentiment.
+### Downstream Use
+- Embedding inside sentiment-driven recommendation engines
+- As a component of multi-task NLP dashboards
+- Fine-tuning for domain-specific sentiment (e.g., medical, finance, hospitality reviews)
 ### Out-of-Scope Use
+- Not designed for languages other than English.
+- Not suited for emotion detection beyond binary sentiment.
 ## Bias, Risks, and Limitations
+This model was fine-tuned on Amazon reviews, which may carry biases toward product-related expressions and cultural language patterns.
+Users should be cautious when applying the model outside typical e-commerce datasets.
 ### Recommendations
+For more robust domain generalization:
+- Further fine-tuning on task-specific datasets is advised.
 ## How to Get Started with the Model
+```python
+from transformers import pipeline
+classifier = pipeline(
+    "sentiment-analysis",
+    model="goatley/sentiment-final-model",
+    tokenizer="goatley/sentiment-final-model"
+)
+classifier(["I love this!", "This was awful."])
+Training Details
+Training Data
+Subset of Amazon Reviews Dataset
+Balanced 50/50 split of positive and negative reviews
+Approximately 5,000 examples used for fine-tuning
+Training Procedure
+Fine-tuned for 3 epochs
+Learning rate scheduling with warmup
+Optimizer: AdamW
+Batch size: 16
+Device: CPU-based training (GitHub Codespaces)
+Training Hyperparameters
+Learning Rate: 5e-5
+Optimizer: AdamW
+Max Sequence Length: 512
+Epochs: 3
+Evaluation
+Testing Data
+Held-out test split from the Amazon Reviews dataset
+Metrics
+Metric	Score
+Test Accuracy	85%
+Evaluation was performed using basic classification metrics (accuracy, precision, recall, F1-score).
+Environmental Impact
+Hardware Type: CPU (GitHub Codespaces)
+Hours Used: ~2 hours
+Cloud Provider: GitHub (Microsoft Azure backend)
+Compute Region: North America
+Carbon Emitted: Negligible (very small dataset + CPU-only fine-tuning)
+Technical Specifications
+Model Architecture and Objective
+Architecture: DistilBERT Transformer Encoder
+Task Objective: Sequence classification with 2 labels (positive, negative)
+Compute Infrastructure
+Training performed on GitHub Codespaces virtual machines.
+No GPUs were used.
+Software Environment
+Hugging Face transformers==4.51.3
+Datasets datasets==3.5.0
+PyTorch torch==2.6.0
+Citation
+If you use this model or find it helpful, please cite:
+APA:
+Goatley, K. (2025). Sentiment Analysis Fine-Tuned DistilBERT Model [Model]. Hugging Face. https://huggingface.co/goatley/sentiment-final-model
+BibTeX:
+@misc{goatley2025sentiment,
+  author = {Keith Goatley},
+  title = {Sentiment
+Analysis Fine-Tuned DistilBERT Model},
+  year = {2025},
+  publisher = {Hugging Face},
+  howpublished = {\url{https://huggingface.co/goatley/sentiment-final-model}}
+}
+Model Card Authors
+Keith Goatley
+Contact
+For questions or inquiries, please contact via:
+GitHub: https://github.com/Keithgoatley
+Hugging Face: https://huggingface.co/goatley