| --- |
| 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="ohanvi/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 = "ohanvi/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/ohanvi/ohanvi-sentiment-analysis}, |
| } |
| ``` |
|
|
| ## Acknowledgements |
|
|
| Built with 🤗 [Transformers](https://github.com/huggingface/transformers), |
| 🤗 [Datasets](https://github.com/huggingface/datasets), and |
| [Gradio](https://gradio.app/). |
|
|
