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README.md

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+
+# XLM-RoBERTa-Base Multilingual Model for Sentiment Analysis on Amazon Reviews
+
+This repository contains a multilingual sentiment analysis model fine-tuned on the [Amazon Reviews Multi](https://huggingface.co/datasets/amazon_reviews_multi) dataset using the `xlm-roberta-base` architecture from Hugging Face Transformers. The model is capable of analyzing product review sentiment in multiple languages and is suitable for real-world multilingual applications.
+
+---
+
+## Model Details
+
+- **Model Architecture:** XLM-RoBERTa Base
+- **Task:** Sentiment Classification (Binary: Positive / Negative)
+- **Dataset:** Amazon Reviews Multi (`en` subset used for fine-tuning)
+- **Languages Supported:** Trained on English, generalizes to multilingual due to XLM-R architecture
+- **Fine-tuning Framework:** Hugging Face Transformers
+
+---
+
+## Usage
+
+### Installation
+
+```bash
+pip install transformers torch
+```
+
+### Loading and Testing the Model
+
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+# Load model and tokenizer
+model_path = "your-username/xlm-roberta-sentiment-amazon-reviews"
+model = AutoModelForSequenceClassification.from_pretrained(model_path)
+tokenizer = AutoTokenizer.from_pretrained(model_path)
+model.eval()
+
+# Prediction function
+def predict_sentiment(texts):
+    inputs = tokenizer(texts, padding=True, truncation=True, return_tensors="pt").to(model.device)
+    with torch.no_grad():
+        outputs = model(**inputs)
+        probs = torch.softmax(outputs.logits, dim=1)
+        preds = torch.argmax(probs, dim=1)
+    
+    label_map = {0: "Negative", 1: "Positive"}
+    results = []
+    for text, pred, prob in zip(texts, preds, probs):
+        results.append({
+            "text": text,
+            "prediction": label_map[pred.item()],
+            "confidence": round(prob[pred].item(), 4)
+        })
+    return results
+
+# Example
+examples = ["This product is amazing!", "Worst purchase ever."]
+print(predict_sentiment(examples))
+```
+
+---
+
+## Performance Metrics
+
+| Epoch | Training Loss | Validation Loss | Accuracy | F1 Macro |
+|-------|----------------|------------------|----------|-----------|
+| 1     | 0.1987         | 0.1842           | 93.22%   | 0.9321    |
+| 2     | 0.1472         | 0.1987           | 93.46%   | 0.9346    |
+| 3     | 0.0960         | 0.2491           | 93.42%   | 0.9341    |
+
+---
+
+## Fine-Tuning Details
+
+### Dataset
+
+- Source: [Amazon Reviews Multi](https://huggingface.co/datasets/amazon_reviews_multi)
+- Labels: Originally 5 classes; remapped to binary sentiment (0 = Negative [1–2 stars], 1 = Positive [4–5 stars])
+- Neutral (3 stars) were excluded from training
+
+### Training Configuration
+
+- Epochs: 3
+- Batch size: 16
+- Learning rate: 2e-5
+- Optimizer: AdamW
+- Evaluation strategy: Epoch-based
+
+---
+
+## Repository Structure
+
+```
+.
+├── model/                # Fine-tuned model and config files
+├── tokenizer/            # Tokenizer files
+├── inference.py          # Inference and testing script
+├── README.md             # Model documentation
+```
+
+---
+
+## Limitations
+
+- Trained only on the English subset of Amazon Reviews Multi; multilingual performance may vary.
+- Neutral reviews (3-star) are excluded, so the model may not detect nuanced sentiment.
+- Fine-tuning was not domain-specific, so performance may degrade in highly specialized review categories.
+
+---
+
+## Contributing
+
+Contributions are welcome! Feel free to open an issue or pull request for improvements or bug fixes.

config.json

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+{
+  "architectures": [
+    "XLMRobertaForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "classifier_dropout": null,
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "xlm-roberta",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "output_past": true,
+  "pad_token_id": 1,
+  "position_embedding_type": "absolute",
+  "problem_type": "single_label_classification",
+  "torch_dtype": "float16",
+  "transformers_version": "4.51.3",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 250002
+}

model.safetensors

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sentencepiece.bpe.model

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special_tokens_map.json

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+}

tokenizer_config.json

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+  "sp_model_kwargs": {},
+  "tokenizer_class": "XLMRobertaTokenizer",
+  "unk_token": "<unk>"
+}