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---
license: cc
datasets:
- vector-institute/NMB-Plus-Named-Entities
base_model:
- distilbert/distilbert-base-uncased
pipeline_tag: token-classification
tags:
- ner
- bias_detection
model-index:
- name: nmb-plus-bias-ner-bert
results:
- task:
type: named-entity-recognition
name: Named Entity Recognition (NER)
dataset:
type: vector-institute/NMB-Plus-Named-Entities
name: Biased Named Entities
metrics:
- type: precision
value: 0.6405
- type: recall
value: 0.5589
- type: f1
value: 0.5922
language:
- en
---
# Model Overview
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.
## License
CC BY 4.0 (Creative Commons Attribution 4.0): Allows sharing and adaptation with proper credit. |