| | --- |
| | 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** fine-tuned using **BertForSequenceClassification** on the **Amazon Reviews dataset**. |
| | It classifies Amazon product reviews into sentiment categories: negative, neutral, or positive. |
| | Intended for **research, educational, and non-commercial use only**. |
| |
|
| | --- |
| |
|
| | ## Base Model |
| | * **bert-base-uncased** |
| | * Architecture: Transformer (BERT) |
| | * Head: Sequence Classification |
| | --- |
| |
|
| | ## Intended Use |
| |
|
| | ### ✅ Allowed Uses |
| | * Academic research |
| | * Educational projects |
| | * Personal learning |
| | * Non-commercial applications |
| | * Experiments and benchmarking |
| |
|
| | ### ❌ Prohibited Uses |
| | * Commercial use |
| | * Selling or reselling the model |
| | * Paid APIs or SaaS products |
| | * Monetized applications or services |
| |
|
| | Commercial use is **strictly prohibited** under the CC BY-NC 4.0 license. |
| |
|
| | --- |
| |
|
| | ## Training Data |
| | Trained on the **Amazon Reviews dataset**: |
| | * Language: English |
| | * Domain: E-commerce product reviews |
| | * Data type: Text reviews with sentiment labels |
| |
|
| | The original dataset creators retain all rights to the data. Users should consult the dataset’s original license for details. |
| |
|
| | --- |
| |
|
| | ## Training Procedure |
| | * Model: BertForSequenceClassification |
| | * Framework: Hugging Face Transformers |
| | * Number of labels: 3 |
| | * Loss Function: Cross-entropy loss |
| | * Training performed on GPU if available, otherwise CPU |
| |
|
| | ### Label Mapping |
| | | Label ID | Sentiment | |
| | | -------- | --------- | |
| | | 0 | Negative | |
| | | 1 | Neutral | |
| | | 2 | Positive | |
| |
|
| | --- |
| |
|
| | ## Evaluation |
| | Evaluated using a **multi-class classification report** with three categories: |
| | * Negative |
| | * Neutral |
| | * Positive |
| |
|
| | Metrics include precision, recall, F1-score, and support (per class). |
| | Performance may vary depending on product category and review style. |
| |
|
| | --- |
| |
|
| | ## Limitations and Bias |
| | * Reflects biases in Amazon reviews |
| | * May not perform well on non-product text |
| | * Not suitable for non-English languages |
| | * Predictions are subjective, not factual judgments |
| | --- |
| |
|
| | ## Ethical Considerations |
| | Analyze subjective content only; not for high-stakes decisions. |
| |
|
| | --- |
| |
|
| | ## 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 |
| | Released under **CC BY-NC 4.0**. Commercial use, resale, or monetization is prohibited. |
| | Full license: [https://creativecommons.org/licenses/by-nc/4.0/](https://creativecommons.org/licenses/by-nc/4.0/) |
| |
|
| | --- |
| |
|
| | ## Citation |
| | ```bibtex |
| | @misc{sentiment-analysis-model, |
| | author = {Mian Zaka}, |
| | title = {Amazon Reviews Sentiment Analysis Model}, |
| | year = {2026}, |
| | publisher = {Hugging Face} |
| | } |
| | ``` |
| |
|
| | --- |
| |
|
| | ## Contact |
| | For questions or feedback, contact the model author via Hugging Face. |
