| | ---
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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
|
| | ---
|
| | # Trading Hero Financial Sentiment Analysis
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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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| |
|
| | ## 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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| |
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| | Training Set: 10,918,272 tokens
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| |
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| | Validation Set: 1,213,184 tokens
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| |
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| | Test Set: 1,347,968 tokens
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| |
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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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| |
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| | Corporate Reports (10-K & 10-Q): 2.5 billion tokens
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| |
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| | Earnings Call Transcripts: 1.3 billion tokens
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| |
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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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| |
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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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| |
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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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| |
|
| | ## 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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| | ``` |
