Financialbert Sentiment Analysis

🚀 FinancialBERT for Sentiment Analysis

FinancialBERT is a pre - trained BERT model on a large financial text corpus. It aims to boost financial NLP research and practice in the financial domain, enabling financial practitioners and researchers to leverage this model without the need for substantial computational resources for training.

🚀 Quick Start

The model can be used via the Transformers pipeline for sentiment analysis.

💻 Usage Examples

Basic Usage

from transformers import BertTokenizer, BertForSequenceClassification
from transformers import pipeline

model = BertForSequenceClassification.from_pretrained("ahmedrachid/FinancialBERT-Sentiment-Analysis",num_labels=3)
tokenizer = BertTokenizer.from_pretrained("ahmedrachid/FinancialBERT-Sentiment-Analysis")

nlp = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer)

sentences = ["Operating profit rose to EUR 13.1 mn from EUR 8.7 mn in the corresponding period in 2007 representing 7.7 % of net sales.",  
             "Bids or offers include at least 1,000 shares and the value of the shares must correspond to at least EUR 4,000.", 
             "Raute reported a loss per share of EUR 0.86 for the first half of 2009 , against EPS of EUR 0.74 in the corresponding period of 2008.", 
             ]
results = nlp(sentences)
print(results)

[{'label': 'positive', 'score': 0.9998133778572083},
 {'label': 'neutral', 'score': 0.9997822642326355},
 {'label': 'negative', 'score': 0.9877365231513977}]

✨ Features

FinancialBERT is pre - trained on a large financial text corpus. It was fine - tuned for the Sentiment Analysis task on the Financial PhraseBank dataset. Experiments indicate that this model outperforms general BERT and other financial domain - specific models.

📦 Installation

No specific installation steps are provided in the original README.

📚 Documentation

More details on FinancialBERT's pre - training process can be found at: https://www.researchgate.net/publication/358284785_FinancialBERT_-_A_Pretrained_Language_Model_for_Financial_Text_Mining

🔧 Technical Details

Training data

FinancialBERT model was fine - tuned on Financial PhraseBank, a dataset consisting of 4840 Financial News categorised by sentiment (negative, neutral, positive).

Fine - tuning hyper - parameters

• learning_rate = 2e - 5
• batch_size = 32
• max_seq_length = 512
• num_train_epochs = 5

Evaluation metrics

The evaluation metrics used are: Precision, Recall and F1 - score. The following is the classification report on the test set.

📄 License

No license information is provided in the original README.

Created by Ahmed Rachid Hazourli