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---
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
## Citation
Please refer to this repo when using the model. |