language:
- en
license: mit
tags:
- sentiment-analysis
- roberta
- text-classification
- amazon-reviews
- pytorch
datasets:
- amazon_polarity
pipeline_tag: text-classification
widget:
- text: This product is absolutely amazing! Very high quality.
example_title: Positive Review
- text: Terrible quality. Arrived broken and stopped working.
example_title: Negative Review
model-index:
- name: amazon-roberta-sentiment
results:
- task:
type: text-classification
name: Sentiment Analysis
dataset:
name: Amazon Polarity
type: amazon_polarity
metrics:
- type: accuracy
value: 0.9697
- type: f1
value: 0.97
- type: precision
value: 0.9671
- type: recall
value: 0.973
metrics:
- f1
- accuracy
- recall
- precision
base_model:
- FacebookAI/roberta-base
library_name: transformers
Amazon RoBERTa Sentiment
A fine-tuned RoBERTa-base model that classifies Amazon product reviews as Positive or Negative.
- Repo / Code: Github
- Live Demo: reviews-sentiment-analysis-s7brv3.streamlit.app
Model Details
Model Description
This model is a fine-tuned version of roberta-base trained on the Amazon Polarity dataset for binary sentiment classification (Positive / Negative). The title and content fields of each review were used jointly as model input.
The final model was selected after three training experiments comparing architectures and regularization strategies — see Training Details below. (Or the full details of the experiment, including all three rounds, on GitHub).
- Developed by: Yousef Eisa
- Model type: Text Classification (binary sentiment)
- Language(s): English
- License: MIT
- Finetuned from model:
roberta-base
Uses
Direct Use
The model takes a review's title and body text and outputs a sentiment label (Positive / Negative) with a confidence score. It can be used directly for classifying Amazon-style product reviews, or similar English-language e-commerce review text.
Out-of-Scope Use
- Not trained or evaluated on non-English text.
- Binary classification only — there is no "neutral" class, so mixed or ambiguous reviews will be forced into Positive/Negative.
How to Get Started with the Model
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
model_name = "YousefXEisa/amazon-roberta-sentiment"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
text = "This product is absolutely amazing! Very high quality."
inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
with torch.no_grad():
logits = model(**inputs).logits
probs = torch.softmax(logits, dim=-1)
label = "Positive" if probs.argmax().item() == 1 else "Negative"
print(label, probs)
Or with the pipeline API:
from transformers import pipeline
classifier = pipeline("text-classification", model="YousefXEisa/amazon-roberta-sentiment")
classifier("Terrible quality. Arrived broken and stopped working.")
Training Details
Training Data
Amazon Polarity — Amazon product reviews labeled Positive/Negative. title and content were concatenated as input.
Training Procedure
The final model (RoBERTa-base) was trained in fp32 on 250k samples for 3 epochs (early stopping enabled), with the following regularization recipe, arrived at after diagnosing overfitting in an initial BERT baseline run:
- Frozen embedding layer + first 4 encoder layers
- Dropout 0.1 on hidden and attention layers
- Label smoothing (0.1)
- Weight decay (0.01), excluded from bias/LayerNorm params
- Cosine LR schedule with 10% warmup
- Gradient clipping (
max_norm=1.0) - Early stopping (patience=2, delta=0.001)
Training was run on Google Colab (free GPU tier).
Evaluation
Evaluated on a held-out test set of 10,000 samples, unseen during training or validation.
Results
| Metric | Score |
|---|---|
| Accuracy | 0.9697 |
| Precision | 0.9671 |
| Recall | 0.9730 |
| F1 Score | 0.9700 |
Classification Report:
precision recall f1-score support
0 0.97 0.97 0.97 4958
1 0.97 0.97 0.97 5042
accuracy 0.97 10000
macro avg 0.97 0.97 0.97 10000
weighted avg 0.97 0.97 0.97 10000
RoBERTa outperformed a comparably-regularized BERT baseline (best val F1 0.9674 vs 0.9590) trained under identical conditions, which is why RoBERTa was selected as the final model.
Bias, Risks, and Limitations
- Trained and evaluated on English Amazon product reviews only; accuracy on other domains or languages is untested.
- Binary classification only (Positive/Negative) — no neutral class.
- Like most sentiment models trained on product reviews, it may be less reliable on sarcasm, mixed sentiment within a single review, or very short/ambiguous text.
Environmental Impact
- Hardware Type: Google Colab GPU (free tier)
- Cloud Provider: Google Colab
- Carbon emissions were not tracked for this project. They can be estimated using the ML Impact calculator.
Citation
If you use this model, please reference the Hugging Face repo:
YousefXEisa/amazon-roberta-sentiment