| ---
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| license: mit
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| tags:
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| - sentiment analysis
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| - financial sentiment analysis
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| - bert
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| - text-classification
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| - finance
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| - finbert
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| - financial
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| ---
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| # Trading Hero Financial Sentiment Analysis
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|
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| Model Description: This model is a fine-tuned version of [FinBERT](https://huggingface.co/yiyanghkust/finbert-pretrain), a BERT model pre-trained on financial texts. The fine-tuning process was conducted to adapt the model to specific financial NLP tasks, enhancing its performance on domain-specific applications for sentiment analysis.
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|
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| ## Model Use
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|
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| Primary Users: Financial analysts, NLP researchers, and developers working on financial data.
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|
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| ## Training Data
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|
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| Training Dataset: The model was fine-tuned on a custom dataset of financial communication texts. The dataset was split into training, validation, and test sets as follows:
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| Training Set: 10,918,272 tokens
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| Validation Set: 1,213,184 tokens
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| Test Set: 1,347,968 tokens
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| Pre-training Dataset: FinBERT was pre-trained on a large financial corpus totaling 4.9 billion tokens, including:
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| Corporate Reports (10-K & 10-Q): 2.5 billion tokens
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| Earnings Call Transcripts: 1.3 billion tokens
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| Analyst Reports: 1.1 billion tokens
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|
|
| ## Evaluation
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|
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| * Test Accuracy = 0.908469
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| * Test Precision = 0.927788
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| * Test Recall = 0.908469
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| * Test F1 = 0.913267
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| * **Labels**: 0 -> Neutral; 1 -> Positive; 2 -> Negative
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|
|
|
|
| ## Usage
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|
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| ```
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| import torch
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| from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
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| tokenizer = AutoTokenizer.from_pretrained("fuchenru/Trading-Hero-LLM")
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| model = AutoModelForSequenceClassification.from_pretrained("fuchenru/Trading-Hero-LLM")
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| nlp = pipeline("text-classification", model=model, tokenizer=tokenizer)
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| # Preprocess the input text
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| def preprocess(text, tokenizer, max_length=128):
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| inputs = tokenizer(text, truncation=True, padding='max_length', max_length=max_length, return_tensors='pt')
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| return inputs
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|
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| # Function to perform prediction
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| def predict_sentiment(input_text):
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| # Tokenize the input text
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| inputs = tokenizer(input_text, return_tensors="pt", truncation=True, padding=True)
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|
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| # Perform inference
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| with torch.no_grad():
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| outputs = model(**inputs)
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|
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| # Get predicted label
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| predicted_label = torch.argmax(outputs.logits, dim=1).item()
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|
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| # Map the predicted label to the original labels
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| label_map = {0: 'neutral', 1: 'positive', 2: 'negative'}
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| predicted_sentiment = label_map[predicted_label]
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|
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| return predicted_sentiment
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|
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| stock_news = [
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| "Market analysts predict a stable outlook for the coming weeks.",
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| "The market remained relatively flat today, with minimal movement in stock prices.",
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| "Investor sentiment improved following news of a potential trade deal.",
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| .......
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| ]
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| for i in stock_news:
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| predicted_sentiment = predict_sentiment(i)
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| print("Predicted Sentiment:", predicted_sentiment)
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| ```
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| ```
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| Predicted Sentiment: neutral
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| Predicted Sentiment: neutral
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| Predicted Sentiment: positive
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| ```
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|
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| ## Citation
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|
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| ```
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| @misc{yang2020finbert,
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| title={FinBERT: A Pretrained Language Model for Financial Communications},
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| author={Yi Yang and Mark Christopher Siy UY and Allen Huang},
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| year={2020},
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| eprint={2006.08097},
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| archivePrefix={arXiv},
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| }
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| ``` |
