Create README.md

README.md

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+---
+language: 
+  - en
+thumbnail:  https://cdn.pixabay.com/photo/2017/09/07/08/54/money-2724241__340.jpg
+tags:
+- text-classification
+- sentiment-analysis
+- finance-sentiment-detection
+- finance-sentiment
+license: apache-2.0
+datasets:
+- cyrilzhang/financial_phrasebank_split
+metrics:
+- Accuracy, F1 score
+widget:
+- text: "HK stocks open lower after Fed rate comments"
+  example_title: "HK stocks open lower"
+- text: "US stocks end lower on earnings worries"
+  example_title: "US stocks end lower"
+- text: "Muted Fed, AI hopes send Wall Street higher"  
+  example_title: "Muted Fed"
+
+---
+## nickwong64/bert-base-uncased-finance-sentiment
+Bert is a Transformer Bidirectional Encoder based Architecture trained on MLM(Mask Language Modeling) objective.
+[bert-base-uncased](https://huggingface.co/bert-base-uncased) finetuned on the [cyrilzhang/financial_phrasebank_split](https://huggingface.co/datasets/cyrilzhang/financial_phrasebank_split) dataset using HuggingFace Trainer with below training parameters.
+```
+ learning rate 2e-5, 
+ batch size 8,
+ num_train_epochs=6,
+```
+
+ ## Model Performance
+| Epoch | Training Loss | Validation Loss | Accuracy | F1 |
+| --- | --- | --- | --- | --- |
+| 6 | 0.034100	| 0.954745 | 0.853608 | 0.854358 |
+
+
+
+ ## How to Use the Model
+```python 
+from transformers import pipeline
+nlp = pipeline(task='text-classification', 
+               model='nickwong64/bert-base-uncased-finance-sentiment')
+p1 = "HK stocks open lower after Fed rate comments"
+p2 = "US stocks end lower on earnings worries"
+p3 = "Muted Fed, AI hopes send Wall Street higher"
+print(nlp(p1))
+print(nlp(p2))
+print(nlp(p3))
+"""
+output:
+[{'label': 'negative', 'score': 0.9991507530212402}]
+[{'label': 'negative', 'score': 0.9997240900993347}]
+[{'label': 'neutral', 'score': 0.9834381937980652}]
+"""
+```
+
+## Dataset
+  [cyrilzhang/financial_phrasebank_split](https://huggingface.co/datasets/cyrilzhang/financial_phrasebank_split) 
+
+## Labels
+```
+{0: 'negative', 1: 'neutral', 2: 'positive'}
+```
+
+## Evaluation
+```
+{'test_loss': 0.9547446370124817,
+ 'test_accuracy': 0.8536082474226804,
+ 'test_f1': 0.8543579048224414,
+ 'test_runtime': 4.9865,
+ 'test_samples_per_second': 97.263,
+ 'test_steps_per_second': 12.233}
+```
+