Update README.md

README.md

@@ -27,3 +27,95 @@ model-index:
         value: 0.5922
 
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+
+# Model Card for Model ID
+
+A fine-tuned DistilBERT model for Named Entity Recognition (NER) in bias detection
+
+## Model Details
+We used `distilbert-base-uncased` and fine-tuned it on `vector-institute/NMB-Plus-Named-Entities` dataset.
+
+
+
+## How to Get Started with the Model
+
+```python
+from transformers import AutoModelForTokenClassification, AutoTokenizer
+
+model_name = "vector-institute/nmb-plus-bias-ner-bert"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+label_list = ["O", "B-BIAS", "I-BIAS"]
+id2label = {i: label for i, label in enumerate(label_list)}
+label2id = {label: i for i, label in enumerate(label_list)}
+
+model = AutoModelForTokenClassification.from_pretrained(
+    model_name,
+    id2label=id2label, 
+    label2id=label2id
+)
+
+
+ner_pipeline = pipeline("ner", model=model, tokenizer=tokenizer)
+
+text = "Fox News reported that Joe Biden met with CNN executives."
+predictions = ner_pipeline(text)
+print(predictions)
+
+```
+
+
+## Training Hyperparameters
+
+- **Training regime:**
+Here's the training arguments we used:
+
+```python
+training_args = TrainingArguments(
+    learning_rate=2e-5,
+    per_device_train_batch_size=64,
+    per_device_eval_batch_size=32,
+    num_train_epochs=10,
+    weight_decay=0.01,
+    eval_strategy="epoch",
+    save_strategy="epoch",
+    load_best_model_at_end=True,
+    output_dir="./results",
+    logging_dir="./logs",
+    logging_steps=50,
+    group_by_length=True,
+)
+```
+
+
+## Evaluation
+
+We split the data to train(80%), validation(10%) and test(10%) sets.
+
+
+### Results
+We used common classification metrics:
+- precision
+- recall
+- f1-score
+
+#### Overall Results:
+| Metric         | Precision | Recall | F1-Score | Support |
+|---------------|-----------|--------|----------|---------|
+| **Macro Avg** | 0.6405    | 0.5589 | 0.5922   | 48710   |
+| **Weighted Avg** | 0.9330 | 0.9418 | 0.9366   | 48710   |
+
+#### Per-class Results:
+
+| Label    | Precision | Recall | F1-Score | Support |
+|----------|-----------|--------|----------|---------|
+| **O**    | 0.9615    | 0.9792 | 0.9703   | 45921   |
+| **B-BIAS** | 0.5314  | 0.4183 | 0.4681   | 930     |
+| **I-BIAS** | 0.4286  | 0.2792 | 0.3381   | 1859    |
+
+
+## Environmental Impact
+
+Total energy consumption for fine-tuning is 0.032804 kWh
+
+**Local CO2 Emission:**  Approximately 3.12 grams of CO₂ equivalent.