Harsh-Gupta
/

Sentiment-Analysis-BERT-sst2

@@ -1,6 +1,28 @@
 ---
 library_name: transformers
 license: mit
 datasets:
 - stanfordnlp/sst2
 language:
@@ -12,197 +34,80 @@ base_model:
 pipeline_tag: text-classification
 ---
-# Model Card for Model ID
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 ---
 library_name: transformers
 license: mit
+tags:
+  - sentiment-analysis
+  - bert
+  - lora
+  - peft
+  - huggingface
+  - transformers
+  - text-classification
+  - low-resource
+model-index:
+  - name: LoRA-BERT for Sentiment Analysis (SST-2)
+    results:
+      - task:
+          type: text-classification
+          name: Sentiment Analysis
+        dataset:
+          type: glue
+          name: SST2
+        metrics:
+          - type: accuracy
+            value: 0.9117
+            name: Accuracy
 datasets:
 - stanfordnlp/sst2
 language:
 pipeline_tag: text-classification
 ---
+# 🤖 LoRA-BERT for Sentiment Analysis (SST-2)
+This is a lightweight, parameter-efficient BERT model fine-tuned with [LoRA (Low-Rank Adaptation)](https://arxiv.org/abs/2106.09685) for binary sentiment classification on the SST-2 dataset.
+---
+## 💡 Model Highlights
+- ✅ Fine-tuned using **LoRA** (r=8, α=16) on top of `bert-base-uncased`
+- ✅ Trained on [SST2](https://huggingface.co/datasets/stanfordnlp/sst2)
+- ✅ Achieves ~91.17% validation accuracy
+- ✅ Lightweight: only LoRA adapter weights are updated
+---
+## 📊 Results
+| Epoch | Training Loss | Validation Loss | Accuracy |
+|-------|---------------|-----------------|----------|
+| 1     | 0.3030        | 0.2467          | 89.91%   |
+| 2     | 0.1972        | 0.2424          | 90.94%   |
+| 3     | 0.2083        | 0.2395          | 91.17%   |
+| 4     | 0.1936        | 0.2464          | 90.94%   |
+| 5     | 0.1914        | 0.2491          | 90.83%   |
+Early stopping could be applied from Epoch 3 based on validation metrics.
+---
+## 🛠️ Usage
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+from peft import PeftModel, PeftConfig
+model_id = "Harsh-Gupta/bert-lora-sentiment"
+# Load PEFT config + model
+config = PeftConfig.from_pretrained(model_id)
+base_model = AutoModelForSequenceClassification.from_pretrained(config.base_model_name_or_path)
+model = PeftModel.from_pretrained(base_model, model_id)
+# Tokenizer
+tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)
+# Predict
+text = "This movie was absolutely amazing!"
+inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
+with torch.no_grad():
+    outputs = model(**inputs)
+    probs = outputs.logits.softmax(dim=-1)
+    pred = probs.argmax().item()
+```
+---
+## LoRA Configuration
+```python
+LoraConfig(
+    r=32,
+    lora_alpha=4,
+    target_modules=["query", "value"],
+    lora_dropout=0.1,
+    bias="none",
+    task_type="SEQ_CLS"
+)
+```
+---
+## 🔍 Intended Use
+- Sentiment classification for binary text (positive/negative)
+- Can be adapted to other domains: movie reviews, product reviews, tweets
+---
+## 🧠 Author
+Harsh Gupta
+MCA, Jawaharlal Nehru University
+GitHub: [2003Harsh](https://github.com/2003HARSH)