Add model weights

README.md

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+# Financial Relation Extraction
+
+## Process
+
+Detecting the presence of a relationship between financial terms and qualifying the relationship in case of its presence. Example use cases:
+
+* 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>)
+* There are no withdrawal penalties. (<em>Relationship **does not exist**, type: **x**</em>)
+
+## Data
+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.
+A typical row in the dataset consists of a definition sentence and its corresponding relationship label. 
+The labels were restricted to the 5 most-widely identified relationships, namely: **x** (no relationship), **has**, **is in**, **is** and **are**.
+
+
+## Model
+The model used is a standard Roberta-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.
+In addition, the model has been pretrained to initializa weigths that would otherwise be unused if loaded from an existing pretrained stock model.
+
+## Metrics
+The evaluation metrics used are: Precision, Recall and F1-score. The following is the classification report on the test set.
+
+| relation  | precision        | recall           | f1-score  | support  |
+| ------------- |:-------------:|:-------------:|:-------------:| -----:|
+| has | 0.7416      | 0.9674 | 0.8396 | 2362 |
+| is in | 0.7813      | 0.7925 | 0.7869 | 2362 |
+| is | 0.8650     | 0.6863 | 0.7653 | 2362 |
+| are | 0.8365     | 0.8493 | 0.8429 | 2362 |
+| x | 0.9515     | 0.8302 | 0.8867 | 2362 |
+|  |      |  |  |  |
+| macro avg | 0.8352     | 0.8251 | 0.8243 | 11810 |
+| weighted avg | 0.8352     | 0.8251 | 0.8243 | 11810 |

config.json

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training_args.bin

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