README.md

---
license: cc0-1.0
language:
- fi
base_model:
- TurkuNLP/bert-base-finnish-uncased-v1
tags:
- difficulty
- cefr
- regression
- bert
---

# Text Difficulty Regression Model

Regression model which predicts difficulty score for an input text. Predicted scores can be mapped to CEFR levels.

## Model Details

Frozen BERT-large layers with a regressor on top. Trained on a mix of manually annotated datasets (more details on data will follow) and data translated from Russian into Finnish.

## How to Get Started with the Model

Use the code below to get started with the model.

```
class CustomModel(BertPreTrainedModel):
    def __init__(self, config, load_path=None, use_auth_token: str = None,):
        super().__init__(config)
        self.bert = BertModel(config)
        self.pre_classifier = nn.Linear(config.hidden_size, 128)
        self.dropout = nn.Dropout(0.2)
        self.classifier = nn.Linear(128, 1)
        self.activation = nn.ReLU()
        
        nn.init.kaiming_uniform_(self.pre_classifier.weight, nonlinearity='relu')
        nn.init.kaiming_uniform_(self.classifier.weight, nonlinearity='relu')
        if self.pre_classifier.bias is not None:
            nn.init.constant_(self.pre_classifier.bias, 0)
        if self.classifier.bias is not None:
            nn.init.constant_(self.classifier.bias, 0)

    
    def forward(
            self,
            input_ids,
            labels=None,
            attention_mask=None,
            token_type_ids=None,
            position_ids=None,
    ):
        outputs = self.bert(
            input_ids,
            attention_mask=attention_mask,
            token_type_ids=token_type_ids,
            position_ids=position_ids,
        )

        pooled_output = outputs.pooler_output
        pooled_output = self.pre_classifier(pooled_output)
        pooled_output = self.activation(pooled_output)
        pooled_output = self.dropout(pooled_output)  
        logits = self.classifier(pooled_output) 

        if labels is not None:
            loss_fn = nn.MSELoss()
            loss = loss_fn(logits.view(-1), labels.view(-1))
            return loss, logits
        else:
            return None, logits


# Inference 
from safetensors.torch import load_file
# Code to load custom fine-tuned model'
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
config = AutoConfig.from_pretrained(model_path, trust_remote_code=True)
config.num_labels = 1

# Load your custom model
model = CustomModel(config)
state_dict = load_file(f'{model_path}/model.safetensors')
model.load_state_dict(state_dict)
model.eval()

inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True)
inputs = {key: value.to(device) for key, value in inputs.items()}
        
with torch.no_grad():
  _, logits = model(input_ids=inputs["input_ids"], attention_mask=inputs["attention_mask"], token_type_ids=inputs["token_type_ids"])
```


To map to CEFR, use: 
```
reg2cl2 = {
    "0.0": "A1", "1.0": "A1", "1.5": "A1-A2", "2.0": "A2",
    "2.5": "A2-B1", "3.0": "B1", "3.5": "B1-B2", "4.0": "B2",
    "4.5": "B2-C1", "5.0": "C1", "5.5": "C1-C2", "6.0": "C2"
}

print("Predicted output (logits):", logits.item(), reg2cl2[str(float(round(logits.item())))])
```

## Training Details


#### Training Hyperparameters

+ num_warmup_steps = int(0.1 * num_training_steps)
+ num_train_epochs: 24.0
+ batch_size: 16
+ weight_decay: 0.01
+ adam_beta1: 0.9
+ adam_beta2: 0.99
+ adam_epsilon: 1e-8
+ max_grad_norm:  1.0
+ fp16: True
+ early_stopping: True

#### Learning rates

```
# Define separate learning rates
lr_bert = 2e-5  # Learning rate for BERT layers
lr_classifier = 1e-3  # Learning rate for the classifier

optimizer = torch.optim.AdamW([
    {"params": model.bert.parameters(), "lr": lr_bert},  # BERT layers
    {"params": model.classifier.parameters(), "lr": lr_classifier},  
    {"params": model.pre_classifier.parameters(), "lr": lr_classifier},  
])
```

## Evaluation on test set 


![Evaluation results](matrix-reg.png)

## Citation

Please refer to this repo when using the model.