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FinISH (Finance-Identifying Sroberta for Hypernyms)

We present FinISH, a SRoBERTa base model fine-tuned on the FIBO ontology dataset for domain-specific representation learning on the Semantic Search downstream task.

SRoBERTa Model Architecture

Sentence-RoBERTa (SRoBERTa) is a modification of the pretrained RoBERTa network that uses siamese and triplet network structures to derive semantically meaningful sentence embeddings that can be compared using cosine-similarity. This reduces the effort for finding the most similar pair from 65 hours with RoBERTa to about 5 seconds with SRoBERTa, while maintaining the accuracy from RoBERTa. SRoBERTa has been evaluated on common STS tasks and transfer learning tasks, where it outperforms other state-of-the-art sentence embeddings methods.

Paper: Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks.

Authors: Nils Reimers and Iryna Gurevych.

Details on the downstream task (Semantic Search for Text Classification)

The objective of this task is to correctly classify a given term in the financial domain according to its prototypical hypernym in a list of available hypernyms:

  • Bonds
  • Forward
  • Funds
  • Future
  • MMIs (Money Market Instruments)
  • Option
  • Stocks
  • Swap
  • Equity Index
  • Credit Index
  • Securities restrictions
  • Parametric schedules
  • Debt pricing and yields
  • Credit Events
  • Stock Corporation
  • Central Securities Depository
  • Regulatory Agency

This kind-based approach relies on identifying the closest hypernym semantically to the given term (even if they possess common properties with other hypernyms).

Data Description

The data is a scraped list of term definitions from the FIBO ontology website where each definition has been mapped to its closest hypernym from the proposed labels. For multi-sentence definitions, we applied sentence-splitting by punctuation delimiters. We also applied lowercase transformation on all input data.

Data Instances

The dataset contains a label representing the hypernym of the given definition.

{
  'label': 'bonds',
  'definition': 'callable convertible bond is a kind of callable bond, convertible bond.'
}

Data Fields

label: Can be one of the 17 predefined hypernyms.

definition: Financial term definition relating to a concept or object in the financial domain.

Data Splits

The data contains training data with 317101 entries.

Test set metrics

The representational learning model is evaluated on a representative test set with 20% of the entries. The test set is scored based on the following metrics:

  • Average Accuracy
  • Mean Rank (position of the correct label in a set of 5 model predictions)

We evaluate FinISH according to these metrics, where it outperforms other state-of-the-art sentence embeddings methods in this task.

  • Average Accuracy: 0.73
  • Mean Rank: 1.61

Usage (Sentence-Transformers)

Using this model becomes easy when you have sentence-transformers installed:

git clone https://github.com/huggingface/transformers.git
pip install -q ./transformers
pip install -U sentence-transformers

Then you can use the model like this:

from sentence_transformers import SentenceTransformer, util
import torch
model = SentenceTransformer('yseop/roberta-base-finance-hypernym-identification')
# Our corpus containing the list of hypernym labels
hypernyms = ['Bonds',
\t\t\t'Forward',
\t\t\t'Funds',
\t\t\t'Future',
\t\t\t'MMIs',
\t\t\t'Option',
\t\t\t'Stocks',
\t\t\t'Swap',
\t\t\t'Equity Index',
\t\t\t'Credit Index',
\t\t\t'Securities restrictions',
\t\t\t'Parametric schedules',
\t\t\t'Debt pricing and yields',
\t\t\t'Credit Events',
\t\t\t'Stock Corporation',
\t\t\t'Central Securities Depository',
\t\t\t'Regulatory Agency']
hypernym_embeddings = model.encode(hypernyms, convert_to_tensor=True)
# Query sentences are financial terms to match to the predefined labels
queries = ['Convertible bond', 'weighted average coupon', 'Restriction 144-A']
# Find the closest 5 hypernyms of the corpus for each query sentence based on cosine similarity
top_k = min(5, len(hypernyms))
for query in queries:
    query_embedding = model.encode(query, convert_to_tensor=True)
    # We use cosine-similarity and torch.topk to find the highest 5 scores
    cos_scores = util.pytorch_cos_sim(query_embedding, hypernym_embeddings)[0]
    top_results = torch.topk(cos_scores, k=top_k)
    print("\
\
======================\
\
")
    print("Query:", query)
    print("\
Top 5 most similar hypernyms:")
    for score, idx in zip(top_results[0], top_results[1]):
        print(hypernyms[idx], "(Score: {:.4f})".format(score))

Usage (HuggingFace Transformers)

Without sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.

from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
    token_embeddings = model_output[0] #First element of model_output contains all token embeddings
    input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
    return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Query sentences are financial terms to match to the predefined labels
queries = ['Convertible bond', 'weighted average coupon', 'Restriction 144-A']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('yseop/roberta-base-finance-hypernym-identification')
model = AutoModel.from_pretrained('yseop/roberta-base-finance-hypernym-identification')
# Tokenize sentences
encoded_input = tokenizer(queries, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
    model_output = model(**encoded_input)
# Perform pooling
query_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Query embeddings:")
print(query_embeddings)

Created by: Yseop | Pioneer in Natural Language Generation (NLG) technology. Scaling human expertise through Natural Language Generation.

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