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--- |
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inference: true |
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pipeline_tag: text-classification |
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tags: |
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- feature-extraction |
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- text-classification |
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library: pytorch |
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--- |
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<div style="clear: both;"> |
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<div style="float: left; margin-right 1em;"> |
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<h1><strong>FReE (Financial Relation Extraction)</strong></h1> |
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<h2><img src="https://pbs.twimg.com/profile_images/1333760924914753538/fQL4zLUw_400x400.png" alt="" width="25" height="25"></h2> |
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</div> |
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We present FReE, a [DistilBERT](https://huggingface.co/distilbert-base-uncased) base model fine-tuned on a custom financial dataset for financial relation type detection and classification. |
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## Process |
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Detecting the presence of a relationship between financial terms and qualifying the relationship in case of its presence. Example use cases: |
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* An A-B trust is a joint trust created by a married couple for the purpose of minimizing estate taxes. (<em>Relationship **exists**, type: **is**</em>) |
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* There are no withdrawal penalties. (<em>Relationship **does not exist**, type: **x**</em>) |
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## Data |
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The data consists of financial definitions collected from different sources (Wikimedia, IFRS, Investopedia) for financial indicators. Each definition has been split up into sentences, and term relationships in a sentence have been extracted using the [Stanford Open Information Extraction](https://nlp.stanford.edu/software/openie.html) module. |
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A typical row in the dataset consists of a definition sentence and its corresponding relationship label. |
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The labels were restricted to the 5 most-widely identified relationships, namely: **x** (no relationship), **has**, **is in**, **is** and **are**. |
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## Model |
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The model used is a standard DistilBERT-base transformer model from the Hugging Face library. See [HUGGING FACE DistilBERT base model](https://huggingface.co/distilbert-base-uncased) for more details about the model. |
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In addition, the model has been pretrained to initializa weigths that would otherwise be unused if loaded from an existing pretrained stock model. |
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## Metrics |
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The evaluation metrics used are: Precision, Recall and F1-score. The following is the classification report on the test set. |
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| relation | precision | recall | f1-score | support | |
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| ------------- |:-------------:|:-------------:|:-------------:| -----:| |
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| has | 0.7416 | 0.9674 | 0.8396 | 2362 | |
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| is in | 0.7813 | 0.7925 | 0.7869 | 2362 | |
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| is | 0.8650 | 0.6863 | 0.7653 | 2362 | |
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| are | 0.8365 | 0.8493 | 0.8429 | 2362 | |
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| x | 0.9515 | 0.8302 | 0.8867 | 2362 | |
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| macro avg | 0.8352 | 0.8251 | 0.8243 | 11810 | |
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| weighted avg | 0.8352 | 0.8251 | 0.8243 | 11810 | |
