FinRoBERTa-Mendeley

FinRoBERTa-Mendeley

A fine-tuned DistilRoBERTa model for financial sentiment analysis,
trained on a balanced dataset of financial news headlines.

It predicts Positive, Neutral, or Negative sentiment for text related to the stock market, company earnings, or financial trends.

---

Model Details

---

Labels

---

Usage

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
import torch.nn.functional as F

# Load model and tokenizer
model_name = "AurelPx/FinRoBERTa-Mendeley"
device = "cuda" if torch.cuda.is_available() else "cpu"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to(device)

text = "Tesla shares rally after strong earnings report"

# Tokenize and move to correct device
inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(device)

# Predict
with torch.no_grad():
    logits = model(**inputs).logits

# Compute probabilities and predicted label
probs = F.softmax(logits, dim=-1)
pred = probs.argmax().item()

labels = ["Negative", "Neutral", "Positive"]
print(f"Sentence: {text}")
print(f"Predicted Sentiment: {labels[pred]}")
print(f"Probabilities: {probs.cpu().numpy()}")

---

Evaluation

The model was evaluated on a held-out test set of 20% of the dataset.

Performance Comparison

The model shows robust generalization to finance-related texts such as:

• Company earnings releases

• Market commentary

• Analyst outlooks

• Economic updates

---

Citation

@misc{distilroberta_financial_sentiment_mendeley, title={FinRoBERTa-Mendeley}, author={Your Name}, year={2025}, publisher={Hugging Face}, howpublished={\url{https://huggingface.co/your-username/FinRoBERTa-Mendeley}}, note={Fine-tuned DistilRoBERTa for financial sentiment analysis on the Mendeley/Financial News dataset} }

---

Limitations & Future Work

The dataset is limited to English financial headlines.

It may not generalize perfectly to social media or retail investor sentiment (e.g., Reddit).

Next step: multi-source fine-tuning on tweets, Reddit posts, and news summaries.

---

License

MIT License — free for commercial and research use.