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--- |
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tags: |
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- finance |
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--- |
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# Roberta Masked Language Model Trained On Financial Phrasebank Corpus |
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This is a Masked Language Model trained with [Roberta](https://huggingface.co./transformers/model_doc/roberta.html) on a Financial Phrasebank Corpus. |
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The model is built using Huggingface transformers. |
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The model can be found at :[Financial_Roberta](https://huggingface.co./abhilash1910/financial_roberta) |
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## Specifications |
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The corpus for training is taken from the Financial Phrasebank (Malo et al)[https://www.researchgate.net/publication/251231107_Good_Debt_or_Bad_Debt_Detecting_Semantic_Orientations_in_Economic_Texts]. |
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## Model Specification |
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The model chosen for training is [Roberta](https://arxiv.org/abs/1907.11692) with the following specifications: |
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1. vocab_size=56000 |
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2. max_position_embeddings=514 |
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3. num_attention_heads=12 |
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4. num_hidden_layers=6 |
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5. type_vocab_size=1 |
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This is trained by using RobertaConfig from transformers package. |
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The model is trained for 10 epochs with a gpu batch size of 64 units. |
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## Usage Specifications |
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For using this model, we have to first import AutoTokenizer and AutoModelWithLMHead Modules from transformers |
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After that we have to specify, the pre-trained model,which in this case is 'abhilash1910/financial_roberta' for the tokenizers and the model. |
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```python |
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from transformers import AutoTokenizer, AutoModelWithLMHead |
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tokenizer = AutoTokenizer.from_pretrained("abhilash1910/financial_roberta") |
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model = AutoModelWithLMHead.from_pretrained("abhilash1910/financial_roberta") |
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``` |
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After this the model will be downloaded, it will take some time to download all the model files. |
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For testing the model, we have to import pipeline module from transformers and create a masked output model for inference as follows: |
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```python |
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from transformers import pipeline |
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model_mask = pipeline('fill-mask', model='abhilash1910/inancial_roberta') |
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model_mask("The company had a <mask> of 20% in 2020.") |
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``` |
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Some of the examples are also provided with generic financial statements: |
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Example 1: |
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```python |
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model_mask("The company had a <mask> of 20% in 2020.") |
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``` |
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Output: |
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```bash |
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[{'sequence': '<s>The company had a profit of 20% in 2020.</s>', |
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'score': 0.023112965747714043, |
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'token': 421, |
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'token_str': 'Ġprofit'}, |
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{'sequence': '<s>The company had a loss of 20% in 2020.</s>', |
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'score': 0.021379893645644188, |
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'token': 616, |
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'token_str': 'Ġloss'}, |
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{'sequence': '<s>The company had a year of 20% in 2020.</s>', |
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'score': 0.0185744296759367, |
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'token': 443, |
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'token_str': 'Ġyear'}, |
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{'sequence': '<s>The company had a sales of 20% in 2020.</s>', |
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'score': 0.018143286928534508, |
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'token': 428, |
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'token_str': 'Ġsales'}, |
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{'sequence': '<s>The company had a value of 20% in 2020.</s>', |
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'score': 0.015319528989493847, |
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'token': 776, |
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'token_str': 'Ġvalue'}] |
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``` |
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Example 2: |
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```python |
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model_mask("The <mask> is listed under NYSE") |
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``` |
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Output: |
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```bash |
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[{'sequence': '<s>The company is listed under NYSE</s>', |
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'score': 0.1566661298274994, |
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'token': 359, |
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'token_str': 'Ġcompany'}, |
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{'sequence': '<s>The total is listed under NYSE</s>', |
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'score': 0.05542507395148277, |
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'token': 522, |
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'token_str': 'Ġtotal'}, |
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{'sequence': '<s>The value is listed under NYSE</s>', |
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'score': 0.04729423299431801, |
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'token': 776, |
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'token_str': 'Ġvalue'}, |
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{'sequence': '<s>The order is listed under NYSE</s>', |
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'score': 0.02533523552119732, |
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'token': 798, |
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'token_str': 'Ġorder'}, |
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{'sequence': '<s>The contract is listed under NYSE</s>', |
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'score': 0.02087237872183323, |
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'token': 635, |
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'token_str': 'Ġcontract'}] |
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``` |
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## Resources |
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For all resources , please look into the [HuggingFace](https://huggingface.co./) Site and the [Repositories](https://github.com/huggingface). |
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