Model Card for abhilash1910/financial_roberta

model_cards/abhilash1910/financial_roberta/README.md

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