FinDeBERTa: Multi-Label Financial Event Classifier

FinDeBERTa is a fine-tuned DeBERTa-v3-Large model for multi-label financial event classification. It predicts one or more event types from financial news headlines with state-of-the-art performance.

Model Details

• Base Model: microsoft/deberta-v3-large
• Task: Multi-label text classification
• Training: Fine-tuned with Focal Loss and per-class threshold optimization

Event Labels

The model classifies text into 18 financial event types:

["CSR/Brand", "Deal", "Dividend", "Employment", "Expense", "Facility",
 "FinancialReport", "Financing", "Investment", "Legal", "Macroeconomics",
 "Merger/Acquisition", "Product/Service", "Profit/Loss", "Rating", "Revenue",
 "SalesVolume", "SecurityValue"]

Usage

Optimized Usage (with per-class thresholds) (Recommended)

For best performance, use the per-class optimized thresholds:

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
import numpy as np
from huggingface_hub import hf_hub_download

model_name = "ritessshhh/FinDeBERTa"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

# Download per-class thresholds
thresholds_path = hf_hub_download(repo_id=model_name, filename="thresholds.npy")
thresholds = np.load(thresholds_path)

text = "Tesla to acquire a battery startup in a 400 million dollar deal."
inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=128)

with torch.no_grad():
    outputs = model(**inputs)
    probs = torch.sigmoid(outputs.logits)[0].cpu().numpy()

# Apply per-class thresholds
predictions = [
    {"label": model.config.id2label[i], "score": float(prob)}
    for i, prob in enumerate(probs) if prob >= thresholds[i]
]

# Sort by score
predictions = sorted(predictions, key=lambda x: x["score"], reverse=True)
print(predictions)
# Output: [{"label": "Merger/Acquisition", "score": 0.98}, ...]

Basic Usage (with default threshold)

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
import numpy as np

model_name = "ritessshhh/FinDeBERTa"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

text = "Tesla to acquire a battery startup in a 400 million dollar deal."
inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=128)

with torch.no_grad():
    outputs = model(**inputs)
    probs = torch.sigmoid(outputs.logits)[0].cpu().numpy()

# Using default threshold of 0.5
threshold = 0.5
predictions = [
    {"label": model.config.id2label[i], "score": float(prob)}
    for i, prob in enumerate(probs) if prob >= threshold
]

print(predictions)

Training Details

• Loss Function: Focal Loss (gamma=2.0) with dampened class weights
• Optimizer: AdamW with cosine learning rate scheduling
• Batch Size: 8 (with gradient accumulation steps=2)
• Epochs: 10
• Learning Rate: 2e-5
• Weight Decay: 0.02

Performance

Metric	Score
Macro F1	0.692
Micro F1	0.691
Precision (Macro)	0.738
Recall (Macro)	0.691
Exact Match Ratio	0.532

Per-Class Performance

Label	F1 Score	Precision	Recall
Dividend	1.000	1.000	1.000
Employment	0.923	0.857	1.000
Merger/Acquisition	0.892	0.967	0.829
Profit/Loss	0.833	0.824	0.843
SecurityValue	0.829	0.843	0.815
Rating	0.790	0.800	0.780
Revenue	0.780	0.800	0.762
SalesVolume	0.748	0.833	0.678
Financing	0.714	0.714	0.714
Deal	0.696	0.889	0.571

Limitations

• Optimized for financial news headlines (short text)
• May not generalize well to other domains
• Performance varies by event type (rare events like "Facility" have lower F1)

Citation

If you use this model, please cite:

@misc{findeberta2024,
  author = {ritessshhh},
  title = {FinDeBERTa: Multi-Label Financial Event Classifier},
  year = {2024},
  publisher = {HuggingFace},
  howpublished = {\url{https://huggingface.co/ritessshhh/FinDeBERTa}}
}