Create README.md

README.md

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+---
+language: 
+- en
+library_name: transformers
+tags:
+- ensemble
+- text-classification
+- sentiment-analysis
+- imdb
+license: apache-2.0
+datasets:
+- imdb
+metrics:
+- accuracy
+- f1
+pipeline_tag: text-classification
+base_model: 
+- bert-base-uncased
+model-index:
+- name: BERT IMDb Ensemble for Sentiment Analysis
+  results:
+  - task:
+      type: text-classification
+      name: Sentiment Classification
+    dataset:
+      name: IMDb
+      type: imdb
+      split: test
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.939
+    - name: F1
+      type: f1
+      value: 0.939
+---
+
+# BERT IMDb Ensemble for Sentiment Analysis 🎬🎭
+
+## Model description
+
+This is an **ensemble of 3 BERT-base-uncased models** fine-tuned on the IMDb dataset for **binary sentiment classification** (positive vs. negative reviews).  
+Each model was trained with a different random seed, and predictions are combined using weighted or unweighted averaging for more robust performance.
+
+- **Base model:** `bert-base-uncased`  
+- **Task:** Sentiment classification (binary: 0 = negative, 1 = positive)  
+- **Ensembling strategy:** Weighted logits averaging  
+
+---
+
+## Training procedure
+
+- **Dataset:** IMDb (train/test split from Hugging Face `datasets`)  
+- **Preprocessing:**
+  - Tokenization with `bert-base-uncased`
+  - Truncation at 512 tokens  
+
+- **Hyperparameters:**
+  - Epochs: 2  
+  - Batch size: 8  
+  - Optimizer: AdamW (default in `Trainer`)  
+  - FP16: Enabled  
+  - Seeds: `[42, 123, 999]`  
+
+---
+
+## Evaluation results
+
+Across the three models, results are very consistent:
+
+| Model (Seed) | Epochs | Val. Accuracy | Val. Macro F1 |
+|--------------|--------|---------------|---------------|
+| 42           | 2      | 93.74%        | 0.9374        |
+| 123          | 2      | 93.84%        | 0.9383        |
+| 999          | 2      | 93.98%        | 0.9398        |
+
+**Ensemble performance** (weighted example `[0.2, 0.2, 0.6]`) improves stability and helps reduce variance across seeds.
+
+---
+
+## How to use
+
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+tokenizer = AutoTokenizer.from_pretrained("ByteMeHarder-404/bert-imdb-ensemble")
+model = AutoModelForSequenceClassification.from_pretrained("ByteMeHarder-404/bert-imdb-ensemble")
+
+inputs = tokenizer("This movie was an absolute masterpiece!", return_tensors="pt")
+with torch.no_grad():
+    outputs = model(**inputs)
+    probs = torch.nn.functional.softmax(outputs.logits, dim=-1)
+
+print(probs)  # tensor([[0.01, 0.99]]) -> positive sentiment