We retrained the classification layer of Bert Base for a multi-label classification task on our self-labeled data. The model description of the base model can be found at the link above and the description of the dataset can be found here. The fine-tuning parameters are listed below. This model was the inital model used in our experiment to see if there was any promise in our self-labeling approach

Intended uses & limitations

Intended use for this model is to better understand the nature of different social media websites and the nature of the discourse on that site beyond the usual "positive", "negative", "neutral" sentiment of most models. The labels for the commentary data are as follows:

Argumentative
Opinion
Informational
Expressive
Neutral

We think there is promise in this approach, and as this is the initial step towards a deeper understanding of social commentary, there are several limitations to outline

As there were a total of 70k records, data was primarily labeled by language models, with the prompt including correctly labeled examples and incorrectly labeled examples with the correct label. Three language models were tasked with labeling, and only the majority vote labels were kept. Three-way tie samples were set aside. Future iterations would benefit from more models labeling, and more human labeled examples
When reviewing records were ambiguous or that the classifier incorrectly predicted, it was clear that the labeling scheme is fuzzy in some instances. For instance, many "Opinion" comments can be viewed as "Expressive" "Arguments", leading to ambiguous labeling from models. It would be worth exploring a more nuanced labeling scheme, perhaps splitting "Expressive" into 2-3 labels and Opinion into another 1 or 2
Due to the nature of the project, the commentary data used for training was subject to the following limitations
- Queries were isolated to "politics" or "US politics"
- With one exception, all comment data is dated from Jan 1, 2026 to Feb 12, 2026
- We set a ceiling and a floor for number of comments per post. No posts with under 10 comments were used, and for posts with several comments, we only pulled the most recent 300

Training and evaluation data

A full description of the data can be found here

Training procedure

The full code used for training is below

python

tokenizer = AutoTokenizer.from_pretrained("bert-base_uncased")

# Function to tokenize data with
def tokenize_function(batch):
    return tokenizer(
        batch['text'],
        truncation=True, 
        max_length=512 # Can't be greater than model max length
    )

# Tokenize Data
train_data = dataset['train'].map(tokenize_function, batched=True)
test_data = dataset['test'].map(tokenize_function, batched=True)
valid_data = dataset['valid'].map(tokenize_function, batched=True)

# Convert lists to tensors
train_data.set_format("torch", columns=['input_ids', "attention_mask", "label"])
test_data.set_format("torch", columns=['input_ids', "attention_mask", "label"])
valid_data.set_format("torch", columns=['input_ids', "attention_mask", "label"])

model = AutoModelForSequenceClassification.from_pretrained(
    MODEL_ID,
    num_labels=5, # adjust this based on number of labels you're training on
    device_map='cuda',
    dtype='auto',
    label2id=label2id,
    id2label=id2label
)

# Metric function for evaluation in Trainer
def compute_metrics(eval_pred):
    predictions, labels = eval_pred
    predictions = np.argmax(predictions, axis=1)
    
    return {
        'accuracy': accuracy_score(labels, predictions),
        'f1_macro': f1_score(labels, predictions, average='macro'),
        'f1_weighted': f1_score(labels, predictions, average='weighted')
    }

# Data collator to handle padding dynamically per batch
data_collator = DataCollatorWithPadding(tokenizer=tokenizer)

training_args = TrainingArguments(
    output_dir='./bert-comment',
    num_train_epochs=2,
    per_device_train_batch_size=32,
    per_device_eval_batch_size=64,
    learning_rate=2e-5,
    weight_decay=0.01,
    warmup_steps=300,
    
    # Evaluation & saving
    eval_strategy='epoch',
    save_strategy='epoch',
    load_best_model_at_end=True,
    metric_for_best_model='f1_macro',
    
    # Logging
    logging_steps=100,
    report_to='tensorboard',
    
    # Other
    seed=42,
    fp16=torch.cuda.is_available(),  # Mixed precision if GPU available
)

# Set up Trainer
trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=train_data,
    eval_dataset=valid_data,
    processing_class=tokenizer,
    data_collator=data_collator,
    compute_metrics=compute_metrics
)

# Train!
trainer.train()

# Evaluate
eval_results = trainer.evaluate()
print(eval_results)

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 2e-05
train_batch_size: 32
eval_batch_size: 64
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 300
num_epochs: 2
mixed_precision_training: Native AMP

Training results

As this is a multi-label classification problem and there is class imbalance, the main metric we evaluate this model by is f1_macro

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1 Macro	F1 Weighted
0.6645	1.0	1540	0.6703	0.7275	0.7134	0.7292
0.5152	2.0	3080	0.6531	0.7444	0.7295	0.7451

Framework versions

Transformers 5.0.0
Pytorch 2.10.0+cu128
Datasets 4.0.0
Tokenizers 0.22.2

ADS509/experiment_labels_bert_base

Experiment_labels_bert_base

Model description