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README.md

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----
-license: cc-by-nc-4.0
----
+---
+license: cc-by-nc-4.0
+tags:
+  - sentiment-analysis
+  - text-classification
+  - nlp
+language:
+  - en
+---
+
+# Amazon Reviews Sentiment Analysis Model
+
+## Model Description
+This model is a **sentiment analysis model** trained on the **Amazon Reviews dataset** to classify customer reviews into sentiment categories (e.g., positive, negative, neutral depending on configuration).
+It is designed for:
+* Learning and research purposes
+* NLP experimentation
+* Academic projects
+* Non-commercial applications
+
+The model is based on a **Transformer architecture (BERT-based)** and fine-tuned specifically for sentiment classification tasks.
+
+---
+
+## Intended Use
+
+### ✅ Allowed Uses
+* Academic research
+* Educational projects
+* Personal experimentation
+* Non-commercial applications
+* Benchmarking and evaluation
+
+### ❌ Prohibited Uses
+* Commercial use
+* Selling or reselling the model
+* Monetized APIs or SaaS products
+* Integration into paid software or services
+
+> **Note:** Commercial use is strictly prohibited under the CC BY-NC 4.0 license.
+
+---
+
+## Training Data
+The model was trained on the **Amazon Reviews dataset**, which contains user-generated product reviews and ratings from Amazon.
+* Language: English
+* Domain: E-commerce product reviews
+* Data type: Text reviews with sentiment labels
+
+The original dataset creators retain their respective rights. Please refer to the dataset’s original license and terms for more details.
+
+---
+
+## Training Procedure
+* Model: BertForSequenceClassification
+* Framework: Hugging Face Transformers
+* Number of labels: 3
+* Loss Function: Cross-entropy loss
+* Training was performed using device-agnostic code (GPU if available, otherwise CPU)
+
+### Label Mapping
+The sentiment labels used by the model are mapped as follows:
+
+| Label ID | Sentiment |
+| -------- | --------- |
+| 0        | Negative  |
+| 1        | Neutral   |
+| 2        | Positive  |
+
+---
+
+## Evaluation
+The model was evaluated using a **multi-class classification report** with three sentiment categories:
+* Negative
+* Neutral
+* Positive
+
+Evaluation metrics include:
+* Precision
+* Recall
+* F1-score
+* Support (per class)
+
+The classification report was generated using standard tools such as `sklearn.metrics.classification_report`.
+Performance may vary depending on product category, writing style, and domain shift.
+
+---
+
+## Limitations and Bias
+* The model reflects biases present in Amazon user reviews
+* Performance may degrade on non-product-related text
+* Not suitable for languages other than English
+* May not generalize well to informal or domain-specific slang
+
+Users are encouraged to evaluate the model on their own datasets before deployment.
+
+---
+
+## Ethical Considerations
+* This model analyzes user-generated content, which may include biased or subjective opinions
+* Predictions should not be treated as factual judgments
+* Not intended for high-stakes decision-making
+
+---
+
+## How to Use
+
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+model_name = "mianzaka/sentiment-analysis-model/"
+
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+
+text = "The product quality is decent but delivery was slow."
+
+inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
+
+with torch.no_grad():
+    outputs = model(**inputs)
+
+predicted_label = torch.argmax(outputs.logits, dim=1).item()
+
+label_map = {0: "Negative", 1: "Neutral", 2: "Positive"}
+print("Predicted sentiment:", label_map[predicted_label])
+```
+
+---
+
+## License
+This model is released under the **Creative Commons Attribution-NonCommercial 4.0 (CC BY-NC 4.0)** license.
+**Commercial use, resale, or monetization of this model is strictly prohibited.**
+For more details, see the full license text: [https://creativecommons.org/licenses/by-nc/4.0/](https://creativecommons.org/licenses/by-nc/4.0/)
+
+---
+
+## Citation
+If you use this model in your research or projects, please cite:
+
+```bibtex
+@misc{sentiment-analysis-model,
+  author       = {Mian Zaka},
+  title        = {Amazon Reviews Sentiment Analysis Model},
+  year         = {2026},
+  publisher    = {Hugging Face}
+}
+```
+
+---
+
+## Contact
+For questions, feedback, or licensing inquiries, please contact the model author via Hugging Face.