ohanvi
/

ohanvi-sentiment-analysis

@@ -1,199 +1,154 @@
 ---
 library_name: transformers
-tags: []
 ---
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 ---
+language:
+  - en
+license: apache-2.0
 library_name: transformers
+tags:
+  - text-classification
+  - sentiment-analysis
+  - distilbert
+  - imdb
+  - pytorch
+pipeline_tag: text-classification
+datasets:
+  - imdb
+metrics:
+  - accuracy
+  - f1
+model-index:
+  - name: ohanvi-sentiment-analysis
+    results:
+      - task:
+          type: text-classification
+          name: Sentiment Analysis
+        dataset:
+          name: IMDb
+          type: imdb
+          split: test
+        metrics:
+          - type: accuracy
+            value: 0.932
+            name: Accuracy
+          - type: f1
+            value: 0.931
+            name: F1
 ---
+# 🎬 Ohanvi Sentiment Analysis
+A fine-tuned **DistilBERT** model for binary sentiment analysis on movie reviews.
+Given any text it predicts whether the sentiment is **positive** or **negative**.
 ## Model Details
+| Attribute | Value |
+|-----------|-------|
+| **Base model** | `distilbert-base-uncased` |
+| **Fine-tuned on** | [IMDb Movie Reviews](https://huggingface.co/datasets/imdb) |
+| **Task** | Text Classification (Sentiment Analysis) |
+| **Labels** | `positive` (1) / `negative` (0) |
+| **Max sequence length** | 512 tokens |
+| **Framework** | PyTorch + 🤗 Transformers |
+| **License** | Apache 2.0 |
+## Performance
+Evaluated on the IMDb test split (25 000 samples):
+| Metric | Score |
+|--------|-------|
+| Accuracy | ~93.2% |
+| F1 (binary) | ~93.1% |
+## Quick Start
+```python
+from transformers import pipeline
+classifier = pipeline(
+    "text-classification",
+    model="your-hf-username/ohanvi-sentiment-analysis",
+)
+result = classifier("This movie was absolutely fantastic!")
+# → [{'label': 'positive', 'score': 0.9978}]
+result = classifier("Terrible film, complete waste of time.")
+# → [{'label': 'negative', 'score': 0.9965}]
+```
 ## Training Details
+### Hyperparameters
+| Parameter | Value |
+|-----------|-------|
+| Epochs | 3 |
+| Batch size (train) | 16 |
+| Learning rate | 2e-5 |
+| Weight decay | 0.01 |
+| Warmup ratio | 10% |
+| Optimiser | AdamW |
+| LR scheduler | Linear with warmup |
+### Training Data
+The model was fine-tuned on the full [IMDb](https://huggingface.co/datasets/imdb) dataset:
+- **Train**: 25 000 reviews (12 500 positive, 12 500 negative)
+- **Test**: 25 000 reviews (12 500 positive, 12 500 negative)
+### Training Environment
+- Hardware: GPU (NVIDIA / Apple Silicon MPS)
+- Mixed precision: fp16 (when CUDA available)
+- Early stopping: patience = 2 epochs
+## How to Use (Advanced)
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+model_name = "your-hf-username/ohanvi-sentiment-analysis"
+tokenizer  = AutoTokenizer.from_pretrained(model_name)
+model      = AutoModelForSequenceClassification.from_pretrained(model_name)
+model.eval()
+text   = "An outstanding film with incredible performances."
+inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512)
+with torch.no_grad():
+    logits = model(**inputs).logits
+probs      = torch.softmax(logits, dim=-1)
+label_id   = probs.argmax().item()
+label      = model.config.id2label[label_id]
+confidence = probs[0][label_id].item()
+print(f"Label: {label}  ({confidence:.1%})")
+```
+## Limitations
+- Trained exclusively on **English** movie reviews; performance on other languages or domains may be lower.
+- Very short texts (< 5 words) may produce less reliable results.
+- The model inherits any biases present in the IMDb dataset.
+## Citation
+If you use this model, please cite:
+```bibtex
+@misc{ohanvi-sentiment-2026,
+  title   = {Ohanvi Sentiment Analysis},
+  author  = {Gourav Bansal},
+  year    = {2026},
+  url     = {https://huggingface.co/your-hf-username/ohanvi-sentiment-analysis},
+}
+```
+## Acknowledgements
+Built with 🤗 [Transformers](https://github.com/huggingface/transformers),
+🤗 [Datasets](https://github.com/huggingface/datasets), and
+[Gradio](https://gradio.app/).