In the first part of our exploration, we laid the foundation for evaluating NLP models in the financial landscape. We emphasized the critical role of high-quality datasets and dived into the capabilities of foundational NLP models, particularly distilbert-base-uncased-finetuned-sst-2-english. Understanding the importance of data selection and model choice forms the bedrock for our deeper dive into specialized models tailored for financial analysis.

Comparative Evaluation

We can compare the results of all 3 models against each other to determine which one performs better. The metrics used are Accuracy, precision, recall and F1 score. Accuracy, recall, precision, and F1 score are common performance metrics used to evaluate the performance of classification models, such as in natural language processing tasks like sentiment analysis or text categorization. Each metric provides insights into different aspects of the model’s predictions.

Accuracy is the most basic evaluation metric and represents the overall correctness of the model’s predictions. It is calculated as the ratio of correctly predicted instances (true positives and true negatives) to the total number of instances in the dataset. While accuracy is a useful metric, it can be misleading when dealing with imbalanced datasets where one class dominates the others, leading to high accuracy even if the model performs poorly on the minority class.

Recall, also known as sensitivity or true positive rate, measures the ability of the model to correctly identify all positive instances (true positives) out of all the actual positive instances (true positives + false negatives). It gives insights into the model’s ability to avoid false negatives and capture relevant positive instances. A high recall indicates that the model is effective at identifying positive cases, even if it means having more false positives.

Precision measures the accuracy of the model’s positive predictions by calculating the ratio of true positives to the sum of true positives and false positives. It shows how well the model avoids false positives. A high precision indicates that the model is conservative in its positive predictions and minimizes false alarms.

The F1 score is the harmonic mean of precision and recall and is used to balance both metrics. It provides a single metric that combines precision and recall, allowing a more comprehensive evaluation of the model’s performance. The F1 score is particularly useful in cases where both high precision and high recall are desired, as it penalizes models that prioritize one metric over the other. A higher F1 score indicates a better balance between precision and recall.

Evaluating each model, it is clear that all 3 have a very good true positive rate, resulting in high precision, recall and F1 score across the board. However, DistilBERT has a very low accuracy on the financial dataset while FINBERT has a very low accuracy in the IMDB dataset. This effectively demonstrates the generalization capability of the BERT model when it is not finetuned for any specific domain. It also demonstrates the capability of FINBERT to perform well with financial data as well as its inability to generalize to non-financial data.

FINBERT-tone however, is an entirely different case. It appears to have an increased generalizing capability than its non-fine-tuned variant as demonstrated by its performance on the IMDB dataset. However, it is not as capable as FINBERT when classifying, this is in direct contradiction of the claims of the developers on the hugging face platform who claimed that this model would be more capable than FINBERT for sentiment analysis tasks. This may be attributed to many areas within the methodology. However, it is likely that FinBERT-tone is much more sensitive to tone, and this may have resulted in its inability to perform a simple binary classification. As the ability to gauge nuance increases, the model has deviated from seeing in black and white.

It is important to note, however, that in the evaluation process, we have ignored the neutral labels in the financial sentiment dataset and have ignored the neutral classifications of the models that have a neutral class. Thus, the data may be significantly skewed based on the model’s capability to handle neutral sentiment.

Summarization

bart-large-cnn

Link: https://huggingface.co/facebook/bart-large-cnn

BART (Bidirectional and Auto-Regressive Transformers) is a state-of-the-art language generation model introduced by Facebook AI Research (FAIR). Unlike traditional transformer models that are primarily designed for tasks like language understanding, BART excels in both text generation and comprehension. The model employs a two-step process, consisting of bidirectional pre-training and auto-regressive decoding. During pre-training, BART learns to predict masked words in a bidirectional manner, similar to BERT. However, it also utilizes an auto-regressive decoder to predict subsequent words, enabling it to generate coherent and contextually relevant text.

One of BART’s key strengths lies in its ability to perform various text generation tasks, such as text summarization, machine translation, and question answering, by fine-tuning the pre-trained model on specific datasets. Its auto-regressive nature allows it to generate lengthy and coherent responses, making it particularly effective for tasks requiring context-aware language generation. BART has demonstrated exceptional performance in various natural language processing tasks and has quickly become a popular choice among researchers and developers for its versatility and ability to handle both text comprehension and generation with impressive results.

The BART-large model has 400 million parameters. It contains 12 layers on the encoder and decoder side with 16 attention heads. It has a vocab size of 50264 and takes a maximum input length of 1024.

Choosing BART can be a highly advantageous decision due to its remarkable versatility and prowess in both text comprehension and generation tasks. As a bidirectional and auto-regressive transformer model, BART combines the strengths of pre-training with bidirectional context understanding, similar to BERT, and auto-regressive decoding to generate coherent and contextually relevant text. This unique architecture enables BART to excel in a wide range of natural language processing tasks, such as text summarization, machine translation, and question answering.

More information: https://github.csom/facebookresearch/fairseq/tree/main/examples/bart

Code snippet:

import pandas as pd

import torch

import transformers

from transformers import pipeline

df = pd.read_csv(dataset path)

df.head()

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained(“facebook/bart-large-cnn”, max_length = 1024)

model = AutoModelForSeq2SeqLM.from_pretrained(“facebook/bart-large-cnn”)

nlp = pipeline(“summarization”, model=model, tokenizer=tokenizer, device = 0)

X = df.iloc[:,full text column]

y = df.iloc[:,summary column]

!pip install torchmetrics

from torchmetrics.text.rouge import ROUGEScore

from transformers import pipeline

torch.cuda.set_device(0)

device = torch.device(‘cuda’ if torch.cuda.is_available() else ‘cpu’)

rouge = ROUGEScore()

rougeL = 0

rouge1 = 0

rouge2 = 0

count = 0

wrong_dict = {}

for input_sequence in X:

  try:

    tokenized = tokenizer(input_sequence, max_length = 1024, return_tensors = ‘pt’).to(device)

    summary_ids = model.generate(tokenized[“input_ids”], num_beams=2)

    summary = tokenizer.batch_decode(summary_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]

    # results = nlp(input_sequence)

    # summary = results[0][‘summary_text’]

    rougeL += rouge(summary, y[count])[‘rougeL_fmeasure’].item()

    rouge1 += rouge(summary, y[count])[‘rouge1_fmeasure’].item()

    rouge2 += rouge(summary, y[count])[‘rouge2_fmeasure’].item()

except:

    pass

  count += 1

  print(count,’/2000 complete’, end = ‘\r’)

  # if count == 2000:

  #   break

rouge1_score = rouge1/count

rouge2_score = rouge2/count

rougeL_score = rougeL/count

print(‘\nRougeL fmeasure:’, rougeL_score)

print(‘Rouge1 fmeasure:’, rouge1_score)

print(‘Rouge2 fmeasure:’, rouge2_score)

Financial Summarization-PEGASUS

Link: https://huggingface.co/human-centered-summarization/financial-summarization-pegasus

PEGASUS is an advanced language model developed by Google Research, known for its exceptional capabilities in abstractive text summarization. Unlike extractive summarization, where sentences are selected from the original text, PEGASUS generates concise and coherent summaries by paraphrasing and reorganizing the content. The model’s architecture is built upon the Transformer-based encoder-decoder framework, and it is trained on a large corpus of diverse data to develop a deep understanding of language semantics and coherence.

One of PEGASUS’s key strengths lies in its ability to produce informative and contextually accurate summaries across various domains and languages. By leveraging pre-training and fine-tuning techniques, PEGASUS can be tailored to specific summarization tasks, achieving remarkable performance in summarizing long documents, news articles, and other text types. Its remarkable generalization abilities make it a valuable tool for generating high-quality summaries in scenarios where human-like summarization is essential, such as content curation, document analysis, and information retrieval.

This fine-tuned model of PEGASUS claims to have an improved performance for financial summarization. It contains 16 layers in the encoder and decoder and takes a maximum input length of 512 tokens. The vocab size is 96103, however, the summary length is much shorter than BART.

Selecting PEGASUS can be a highly advantageous decision for tasks requiring abstractive text summarization. Its exceptional capabilities in generating coherent and informative summaries make it an invaluable asset in various domains. Unlike extractive summarization approaches, PEGASUS excels in paraphrasing and reorganizing content, enabling it to produce concise and contextually accurate summaries that capture the essence of the original text.

PEGASUS’s Transformer-based encoder-decoder architecture, combined with extensive pre-training on diverse datasets, equips it with a deep understanding of language semantics and coherence. This extensive training empowers PEGASUS to generalize effectively across different domains and languages, ensuring its performance remains robust and reliable. From summarizing long documents to news articles and more, PEGASUS can be fine-tuned to tailor its summarization abilities to specific tasks, making it an ideal choice for applications that demand human-like summarization quality, such as content curation, document analysis, and knowledge extraction. In summary, PEGASUS’s proficiency in abstractive summarization and its adaptability across diverse domains make it a compelling and powerful choice for tasks that require top-notch language understanding and summarization capabilities.

Code snippet:

!pip install sentencepiece

import sentencepiece as sentencepiece

from transformers import PegasusTokenizer, PegasusForConditionalGeneration

model_name = “human-centered-summarization/financial-summarization-pegasus”

tokenizer = PegasusTokenizer.from_pretrained(model_name)

model = PegasusForConditionalGeneration.from_pretrained(model_name)

import pandas as pd

#bitcoin articles dataset

df = pd.read_csv(dataset path)

df.head()

from transformers import pipeline

nlp = pipeline(“summarization”, model=model, tokenizer=tokenizer, device = 0, max_length=80, min_length=50)

!pip install torchmetrics

from torchmetrics.text.rouge import ROUGEScore

import torch

torch.cuda.set_device(0)

device = torch.device(‘cuda’ if torch.cuda.is_available() else ‘cpu’)

rouge = ROUGEScore()

from pprint import pprint

print(nlp([X[0]])[0][‘summary_text’])

#code for getting metrics on dataset

rougeL = 0

rouge1 = 0

rouge2 = 0

count = 0

wrong_dict = {}

for input_sequence in X:

  try:

    summary = nlp(input_sequence)[0][‘summary_text’]

    rougeL += rouge(summary, y[count])[‘rougeL_fmeasure’].item()

    rouge1 += rouge(summary, y[count])[‘rouge1_fmeasure’].item()

    rouge2 += rouge(summary, y[count])[‘rouge2_fmeasure’].item()

  except:

    pass

  count += 1

  print(count,’/2000 complete’, end = ‘\r’)

  # if count == 5:

  #   break

rouge1_score = rouge1/count

rouge2_score = rouge2/count

rougeL_score = rougeL/count

print(‘\nRougeL fmeasure:’, rougeL_score)

print(‘Rouge1 fmeasure:’, rouge1_score)

print(‘Rouge2 fmeasure:’, rouge2_score)

Comparative Evaluation

We can compare the results of both models against each other to determine which one performs better. The metric used for this was ROUGE score fmeasure. ROUGE (Recall-Oriented Understudy for Gisting Evaluation) is a set of metrics commonly used to evaluate the quality of automatic text summarization. Its primary focus is on measuring the similarity between the generated summary and one or more reference summaries created by humans. ROUGE calculates various metrics, including ROUGE-N, ROUGE-L, and ROUGE-W, each evaluating different aspects of summarization quality.

ROUGE-N measures the n-gram overlap between the generated summary and the reference summary, where “N” represents the number of consecutive words in the n-gram. ROUGE-L, on the other hand, evaluates the longest common subsequence between the generated and reference summaries, considering not only individual words but also the order in which they appear. Lastly, ROUGE-W extends the evaluation to weighted word sequences, accounting for the importance of words in the summaries based on their frequency in the reference summaries.

ROUGE scores are widely used in research and development of automatic summarization systems, as they provide objective and quantitative measures to assess the quality of generated summaries. Higher ROUGE scores indicate better similarity between the generated summary and the human-created references, suggesting that the summarization system produces summaries that capture the essential content and structure of the original text more effectively. However, ROUGE scores should be interpreted alongside other metrics and human evaluation to ensure a comprehensive assessment of the summarization system’s performance.

ROUGE F-measure, often referred to as ROUGE-F1, is a commonly used evaluation metric in automatic text summarization tasks. It is a combination of precision and recall and is calculated as the harmonic mean of these two metrics.

Precision measures the proportion of words in the generated summary that also appear in the reference summary. It represents the ability of the summarization system to avoid producing irrelevant words that do not appear in the human-created reference summary. Recall, on the other hand, measures the proportion of words in the reference summary that are also present in the generated summary. It represents the ability of the summarization system to capture important information from the original text. By taking the harmonic mean of precision and recall, the ROUGE F-measure balances both metrics and provides a single score that evaluates the overall performance of the summarization system. A higher ROUGE F-measure indicates a better balance between precision and recall, suggesting that the summarization system produces summaries that are both concise and comprehensive, capturing the relevant content from the original text effectively.

From the result analysis, it is evident that the BART model outperforms the PEGASUS model. We can attribute this to many factors including the fact that BART can handle a longer token length, making it easier for the model to handle longer dependencies. It may also be due to the training methods each model has been developed with. Or, we can attribute this large variation to BART’s architecture and the advantage of using an autoregressive decoder. Nonetheless, it is clear that BART is the preferred model regardless of what data it is summarizing.

Conclusion

Upon conducting a thorough evaluation of all the models on two distinct datasets, the findings provide robust and well-justified conclusions that bear significant implications for text classification and summarization tasks.

For text classification, the results unambiguously point to FINBERT as the top-performing model. Its exceptional performance in handling financial text data showcases its specialization and domain-specific expertise, making it the ideal choice for financial sentiment analysis. While FINBERT-tone claimed to outperform the base model, this could not be substantiated by the evaluation, raising questions about its purported advantages in text classification tasks. Furthermore, the evaluation demonstrates that DistilBERT and, by extension the BERT base model, exhibit remarkable performance on more general datasets, illustrating their versatility and adaptability to various text classification challenges, including financial data analysis.

Moving to the task of summarization, the evaluation decisively positions BART as the clear winner. Its superior performance across both general and domain-specific datasets sets it apart from other models, including PEGASUS. BART’s abstractive summarization capabilities allow it to generate coherent and informative summaries that capture the essence of the original text, making it the preferred choice for summarization both general and domain specific. Despite its competence, the evaluation indicates that PEGASUS could not contend with BART’s performance in summarization tasks.

In conclusion, the evidence-based conclusions drawn from the rigorous evaluation provide valuable insights for selecting the most suitable models for text classification and summarization tasks. FINBERT shines as the optimal choice for text classification, particularly in financial domains, while BART emerges as the superior model for summarization, showcasing its capabilities in producing accurate and contextually rich summaries. These findings contribute to advancing the understanding of NLP model performance, guiding practitioners, and researchers in making informed decisions, and elevating the effectiveness of NLP applications in diverse real-world scenarios.

The post Evaluating NLP Models for Text Classification and Summarization Tasks in the Financial Landscape part 2 appeared first on Indium.

The financial landscape is an intricate ecosystem, where vast amounts of textual data carry invaluable insights that can influence markets and shape investment decisions. With the rise of Natural Language Processing (NLP) technologies, the financial industry has found a potent ally in processing, comprehending, and extracting actionable intelligence from this wealth of textual information. In pursuit of harnessing the potential of cutting-edge NLP models, this research endeavor embarked on a meticulous evaluation of various NLP models available on the Hugging Face platform. The primary objective was to assess their performance in financial text classification and summarization tasks, two essential pillars of efficient data analysis in the financial domain.

Financial text classification is a critical aspect of sentiment analysis, topic categorization, and predicting market movements. In parallel, summarization techniques hold paramount significance in digesting extensive texts, capturing salient information, and facilitating prompt decision-making in a rapidly evolving market landscape.

To undertake this comprehensive assessment, two appropriate datasets were chosen to assess models for both summarization and classification tasks. For summarization, the datasets selected were the CNN Dailymail dataset to evaluate the models’ capabilities with more general data, and a dataset of bitcoin-related articles to assess the models’ capabilities with finance-related data. For classification, the datasets selected were a dataset of IMDB reviews, and a dataset of financial documents from a variety of different sectors within the financial industry.

The chosen models for this study were:

distilbert-base-uncased-finetuned-sst-2-english

finbert

finbert-tone

bart-large-cnn

financial-summarization-pegasus

These models were obtained from the Hugging Face platform. Hugging Face is a renowned platform that has emerged as a trailblazer in the realm of Natural Language Processing (NLP). At its core, the platform is dedicated to providing a wealth of resources and tools that empower researchers, developers, and NLP enthusiasts to explore, experiment, and innovate in the field of language understanding. Hugging Face offers a vast repository of pre-trained NLP models that have been fine-tuned for a wide range of NLP tasks, enabling users to leverage cutting-edge language models without the need for extensive training. This accessibility has expedited NLP research and development, facilitating the creation of advanced language-based applications and solutions. Moreover, Hugging Face fosters a collaborative environment, encouraging knowledge sharing and community engagement through discussion forums and support networks. Its user-friendly API and open-source libraries further streamline the integration of NLP capabilities into various projects, making sophisticated language processing techniques more accessible and applicable across diverse industries and use cases.

Gathering the Datasets

In the domain of data-driven technologies, the age-old adage “garbage in, garbage out” holds more truth than ever. At the heart of any successful data-driven endeavor lies the foundation of a high-quality dataset. A good dataset forms the bedrock upon which algorithms, models, and analyses rest, playing a pivotal role in shaping the accuracy, reliability, and effectiveness of any data-driven system. Whether it be in the domains of machine learning, artificial intelligence, or statistical analysis, the quality and relevance of the dataset directly influence the outcomes and insights derived from it. Thus, to evaluate the chosen models, it was imperative that the right datasets were chosen. The datasets used in this study were gathered from Kaggle.

For classification, the chosen neutral dataset was the IMDB Movie Review dataset, which contains 50,000 movie reviews and an assigned sentiment score. You can access it here. As for the financial text dataset, the selected dataset was the Financial Sentiment Analysis dataset, comprising over 5,000 financial records with assigned sentiments. You can find it here. It was necessary to remove the neutral values since not all the selected models have a neutral class.

For summarization, the neutral dataset chosen was the CNN Dailymail dataset, which contains 30,000 news articles written by CNN and The Daily Mail. Only the test dataset was utilized for this evaluation, which includes 11,490 articles and their summaries. You can access it here. For the financial text dataset, the Bitcoin – News articles text corpora dataset was used. This dataset encompasses numerous articles about bitcoin gathered from a wide variety of sources, and it can be found here.

Text Classification

Model: distilbert-base-uncased-finetuned-sst-2-english

Link: https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english

BERT (Bidirectional Encoder Representations from Transformers) is a groundbreaking natural language processing model introduced by Google. It revolutionized the field of NLP by employing a bidirectional transformer architecture, allowing the model to understand context from both the left and right sides of a word. Unlike previous models that processed text sequentially, BERT uses a masked language model approach during pre-training, wherein it randomly masks words and learns to predict them based on the surrounding context. This pre-training process enables BERT to capture deep contextual relationships within sentences, making it highly effective for a wide range of NLP tasks, such as sentiment analysis, named entity recognition, and text classification. However, BERT’s large size and computational demands limit its practical deployment in certain resource-constrained scenarios.

DistilBERT: Efficient Alternative to BERT

DistilBERT, on the other hand, addresses BERT’s resource-intensive limitations by distilling its knowledge into a more compact form. Introduced by Hugging Face, DistilBERT employs a knowledge distillation technique, whereby it is trained to mimic the behavior of the larger BERT model. Through this process, unnecessary redundancy in BERT’s parameters is eliminated, resulting in a significantly smaller and faster model without compromising performance. DistilBERT maintains a competitive level of accuracy compared to BERT while reducing memory usage and inference time, making it an attractive choice for applications where computational resources are a constraint. Its effectiveness in various NLP tasks has cemented its position as an efficient and practical alternative to the original BERT model. DistilBERT retains approximately 97% of BERT’s accuracy while being 40% smaller and 60% faster.

Model Details:

• Parameters: 67 million
• Transformer Layers: 6
• Embedding Layer: Included
• Classification Layer: Softmax
• Attention Heads: 12
• Vocabulary Size: 30522
• Maximum Sequence Length: 512 tokens

Choosing DistilBERT for classification tasks can offer a balance between efficiency and performance. Its faster inference, reduced resource requirements, competitive accuracy, and seamless integration make it an attractive option for a wide range of real-world applications where computational efficiency and effectiveness are key considerations.

Code Snippet:

import torch

from transformers import DistilBertTokenizer, DistilBertForSequenceClassification

from transformers import AutoTokenizer, AutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained(“distilbert-base-uncased-finetuned-sst-2-english”)

model = AutoModelForSequenceClassification.from_pretrained(“distilbert-base-uncased-finetuned-sst-2-english”)

import pandas as pd

torch.cuda.set_device(0)

model.cuda()

df = pd.read_csv(dataset path)

df.head()

df.drop(df.loc[df[‘Sentiment’]==’neutral’].index, inplace=True)

X = df.iloc[:,column for sentiment evaluation]

y = df.iloc[:,target sentiment]

device = torch.device(“cuda” if torch.cuda.is_available() else “cpu”)

#metrics for the model

mydict = {‘positive’:1, ‘negative’:0, 1:1, 0:0}

count = 0

correct = 0

wrong = 0

wrong_dict = {}

for input_sequence in X:

  try:

    if y[count] == ‘neutral’:

      raise Exception(“Neutral”)

    inputs = tokenizer(input_sequence, return_tensors=”pt”).to(device)

    with torch.no_grad():

      logits = model(**inputs).logits

    predicted_class_id = logits.argmax().item()

    if predicted_class_id == mydict[y[count]]:

      correct += 1

    else:

      wrong +=1

      wrong_dict[input_sequence] = predicted_class_id

  except:

    pass

  count += 1

  print(count,’/50000 complete’, end = ‘\r’)

  # if count == 20:

  #   break

print(‘\nCorrect:’, correct)

print(‘Wrong:’, wrong)

print(len(wrong_dict))

print(‘Accuracy:’, correct/(correct+wrong))

fp = 0

fn = 0

for x in wrong_dict:

    if wrong_dict[x] == 0:

        fn += 1

    else:

        fp += 1

num_negatives = 0

num_positives = 0

for x in y:

    if x == 0:

        num_negatives += 1

    else:

        num_positives += 1

print(‘Precision:’, (num_positives-fn)/(num_positives-fn + fp))

print(‘Recall:’, (num_positives-fn)/(num_positives-fn + fn))

print(‘F1:’, (2*(num_positives-fn))/(2*(num_positives-fn) + fp + fn))

FinBERT: Specialized Financial Analysis Model

Link: https://huggingface.co/ProsusAI/finbert

FinBERT is a specialized variant of the BERT (Bidirectional Encoder Representations from Transformers) model, tailored specifically for financial text analysis. Developed by Yumo Xu and his team at RoBERTa Financial, FinBERT is pre-trained on a massive corpus of financial news articles, reports, and other domain-specific data. This pre-training process enables FinBERT to acquire a deep understanding of financial language, including intricate terminologies, domain-specific jargon, and market sentiments.

The distinguishing feature of FinBERT lies in its fine-tuning process, where it is adapted to perform specific financial NLP tasks, such as sentiment analysis, stock price prediction, and event classification. By fine-tuning on task-specific datasets, FinBERT gains the ability to extract nuanced financial insights, categorize financial events accurately, and analyze market sentiments effectively. As a result, FinBERT has proven to be a powerful tool for financial professionals, enabling them to make more informed decisions and obtain deeper insights from the vast ocean of financial text data.

FinBERT is pre-trained on a large corpus of financial text data, enabling it to learn the nuances and specific vocabulary of the financial domain. This pre-training process involves predicting missing words in sentences and is supervised using a financial sentiment dataset, which helps the model learn to classify sentiment accurately.

FinBERT Model Details

• Hidden Layers: 12
• Attention Heads: 12
• Maximum Token Input: 512
• Vocabulary Size: 30873

For more detailed information, visit: https://github.com/yya518/FinBERT

Choosing FinBERT can be a highly advantageous decision for financial text analysis due to its domain-specific expertise and fine-tuned capabilities. Unlike general-purpose NLP models, FinBERT is specifically trained on a vast corpus of financial data, granting it a profound understanding of the intricacies and nuances of financial language. This domain-specific knowledge enables FinBERT to accurately interpret financial jargon, capture sentiment nuances, and comprehend market-related events, making it an invaluable asset for tasks such as sentiment analysis, event classification, and financial news summarization.

Moreover, FinBERT’s fine-tuned nature allows it to excel in financial-specific tasks by adapting to the unique characteristics of financial datasets. Through the fine-tuning process, it learns to extract financial insights with precision, providing actionable intelligence for traders, investors, and financial analysts. By leveraging FinBERT, financial professionals can gain a competitive edge, make well-informed decisions, and navigate the complexities of the financial domain with a powerful and specialized language model at their disposal.

Code snippet:

tokenizer = AutoTokenizer.from_pretrained(“ProsusAI/finbert”)

model = AutoModelForSequenceClassification.from_pretrained(“ProsusAI/finbert”)

finbert-tone

Link: https://huggingface.co/yiyanghkust/finbert-tone

FinBERT-tone is an extension of the FinBERT model, designed to address the additional challenge of sentiment analysis in financial text. Developed by the same team at RoBERTa Financial, FinBERT-tone builds upon the foundation of FinBERT by incorporating a novel aspect – capturing the fine-grained tone of financial news articles. Unlike traditional sentiment analysis, which often focuses on binary positive/negative sentiments, FinBERT-tone aims to discern a more nuanced sentiment spectrum, encompassing positive, negative, and neutral tones.

This extension involves training FinBERT-tone on a specialized dataset that includes financial news articles annotated with granular sentiment labels. By fine-tuning on this tone-specific dataset, FinBERT-tone hones its ability to gauge the varying degrees of sentiment in financial text, offering a more comprehensive and accurate sentiment analysis solution for financial professionals. With the capability to interpret subtle sentiment fluctuations in the market, FinBERT-tone empowers users to make well-calibrated decisions and better understand the emotional aspects that influence financial events, making it a valuable tool for sentiment-aware financial analysis.

FINBERT-tone Model Details

• Fine-tuned on: 10,000 manually annotated sentences from analysis reports
• Improved Performance: Better performance on financial tone analysis tasks
• Hidden Layers: 12
• Attention Heads: 12
• Maximum Token Input: 512
• Vocabulary Size: 30873

For more detailed information, visit: https://github.com/yya518/FinBERT

This model was selected because it can prove to be a strategic advantage for financial professionals seeking sophisticated sentiment analysis capabilities. Unlike traditional sentiment analysis models, FinBERT-tone offers a more nuanced approach by capturing the fine-grained tone of financial news articles. Its specialized training on a dataset annotated with granular sentiment labels allows it to discern subtle variations in sentiment, encompassing positive, negative, and neutral tones in financial text. As a result, FinBERT-tone provides a more comprehensive understanding of the emotional undercurrents within the market, empowering users to make well-informed decisions and respond proactively to sentiment shifts.

By leveraging FinBERT-tone, financial analysts, traders, and investors can gain deeper insights into market sentiment and sentiment-driven trends. Its nuanced sentiment analysis enables users to detect shifts in investor confidence, market sentiment, and public opinion, providing a critical edge in navigating the complexities of financial markets. Additionally, the model’s fine-tuned expertise in financial language ensures accurate interpretation of domain-specific jargon and context, making it an invaluable tool for sentiment-aware financial analysis, risk management, and decision-making.

Code Snippet:

from transformers import BertTokenizer, BertForSequenceClassification

from transformers import pipeline

finbert = BertForSequenceClassification.from_pretrained(‘yiyanghkust/finbert-tone’,num_labels=3)

tokenizer = BertTokenizer.from_pretrained(‘yiyanghkust/finbert-tone’)

nlp = pipeline(“sentiment-analysis”, model=finbert, tokenizer=tokenizer, device = 0)

Continue to Part 2 Link:  Evaluating NLP Models for Text Classification and Summarization Tasks in the Financial Landscape – Part 2

Conclusion

In this first part, we’ve delved into the crucial role of high-quality datasets and explored the capabilities of foundational NLP models like distilbert-base-uncased-finetuned-sst-2-english. Understanding the significance of data and model selection sets the stage for our deep dive into specialized models tailored for financial analysis.

Stay tuned for Part 2, where we’ll explore advanced models like FinBERT and FinBERT-tone, designed to provide nuanced sentiment analysis and tone interpretation in the financial domain. These tools empower professionals to gain invaluable insights and make well-informed decisions in a rapidly evolving market landscape.

The post Evaluating NLP Models for Text Classification and Summarization Tasks in the Financial Landscape – Part 1 appeared first on Indium.