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https://github.com/ProsusAI/finBERT

Financial Sentiment Analysis with BERT
https://github.com/ProsusAI/finBERT

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Financial Sentiment Analysis with BERT

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# FinBERT: Financial Sentiment Analysis with BERT



FinBERT sentiment analysis model is now available on Hugging Face model hub. You can get the model [here](https://huggingface.co/ProsusAI/finbert). 



FinBERT is a pre-trained NLP model to analyze sentiment of financial text. It is built by further training

 the [BERT](https://arxiv.org/pdf/1810.04805.pdf) language model in the finance domain, using a large financial corpus and thereby fine-tuning

  it for financial sentiment classification. For the details, please see 

  [FinBERT: Financial Sentiment Analysis with Pre-trained Language Models](https://arxiv.org/pdf/1908.10063.pdf).



**Important Note:** 

FinBERT implementation relies on Hugging Face's `pytorch_pretrained_bert` library and their implementation of BERT for sequence classification tasks. `pytorch_pretrained_bert` is an earlier version of the [`transformers`](https://github.com/huggingface/transformers) library. It is on the top of our priority to migrate the code for FinBERT to `transformers` in the near future.



## Installing

 Install the dependencies by creating the Conda environment `finbert` from the given `environment.yml` file and

 activating it.

```bash

conda env create -f environment.yml

conda activate finbert

```



## Models

FinBERT sentiment analysis model is now available on Hugging Face model hub. You can get the model [here](https://huggingface.co/ProsusAI/finbert). 



Or, you can download the models from the links below:

* [Language model trained on TRC2](https://prosus-public.s3-eu-west-1.amazonaws.com/finbert/language-model/pytorch_model.bin)

* [Sentiment analysis model trained on Financial PhraseBank](https://prosus-public.s3-eu-west-1.amazonaws.com/finbert/finbert-sentiment/pytorch_model.bin)



For both of these model, the workflow should be like this:

* Create a directory for the model. For example: `models/sentiment/`

* Download the model and put it into the directory you just created.

* Put a copy of `config.json` in this same directory. 

* Call the model with `.from_pretrained()`



## Datasets

There are two datasets used for FinBERT. The language model further training is done on a subset of Reuters TRC2 

dataset. This dataset is not public, but researchers can apply for access 

[here](https://trec.nist.gov/data/reuters/reuters.html).



For the sentiment analysis, we used Financial PhraseBank from [Malo et al. (2014)](https://www.researchgate.net/publication/251231107_Good_Debt_or_Bad_Debt_Detecting_Semantic_Orientations_in_Economic_Texts).

 The dataset can be downloaded from this [link](https://www.researchgate.net/profile/Pekka_Malo/publication/251231364_FinancialPhraseBank-v10/data/0c96051eee4fb1d56e000000/FinancialPhraseBank-v10.zip?origin=publication_list).

 If you want to train the model on the same dataset, after downloading it, you should create three files under the 

 `data/sentiment_data` folder as `train.csv`, `validation.csv`, `test.csv`. 

To create these files, do the following steps:

- Download the Financial PhraseBank from the above link.

- Get the path of `Sentences_50Agree.txt` file in the `FinancialPhraseBank-v1.0` zip.

- Run the [datasets script](scripts/datasets.py):

```python scripts/datasets.py --data_path ```



## Training the model

Training is done in `finbert_training.ipynb` notebook. The trained model will

 be saved to `models/classifier_model/finbert-sentiment`. You can find the training parameters in the notebook as follows:

```python

config = Config(   data_dir=cl_data_path,

                   bert_model=bertmodel,

                   num_train_epochs=4.0,

                   model_dir=cl_path,

                   max_seq_length = 64,

                   train_batch_size = 32,

                   learning_rate = 2e-5,

                   output_mode='classification',

                   warm_up_proportion=0.2,

                   local_rank=-1,

                   discriminate=True,

                   gradual_unfreeze=True )

```

The last two parameters `discriminate` and `gradual_unfreeze` determine whether to apply the corresponding technique 

against catastrophic forgetting.



## Getting predictions

We provide a script to quickly get sentiment predictions using FinBERT. Given a .txt file, `predict.py` produces a .csv file including the sentences in the text, corresponding softmax probabilities for three labels, actual prediction and sentiment score (which is calculated with: probability of positive - probability of negative).



Here's an example with the provided example text: `test.txt`. From the command line, simply run:

```bash

python predict.py --text_path test.txt --output_dir output/ --model_path models/classifier_model/finbert-sentiment

```

## Disclaimer

This is not an official Prosus product. It is the outcome of an intern research project in Prosus AI team.

### About Prosus 

Prosus is a global consumer internet group and one of the largest technology investors in the world. Operating and

 investing globally in markets with long-term growth potential, Prosus builds leading consumer internet companies that empower people and enrich communities.

For more information, please visit [www.prosus.com](www.prosus.com).



## Contact information

Please contact Dogu Araci `dogu.araci[at]prosus[dot]com` and Zulkuf Genc `zulkuf.genc[at]prosus[dot]com` about

 any FinBERT related issues and questions.