Update

README.md

@@ -10,31 +10,32 @@ tags:
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 ### Model Overview:
-This NLP model is fine-tuned with a focus on analyzing sentiment in financial text and news headlines. It was trained using the [bert-base-uncased](https://huggingface.co/bert-base-uncased) model on the [financial_phrasebank](https://huggingface.co/datasets/financial_phrasebank) and [auditor_sentiment](https://huggingface.co/datasets/FinanceInc/auditor_sentiment) datasets. It achieves the following accuracies in the trained datasets:
+This NLP model is fine-tuned with a focus on analyzing sentiment in financial text and news headlines. It was trained using the [bert-base-uncased](https://huggingface.co/bert-base-uncased) model on the [financial_phrasebank](https://huggingface.co/datasets/financial_phrasebank) and [auditor_sentiment](https://huggingface.co/datasets/FinanceInc/auditor_sentiment) datasets.
 
-**financial_phrasebank accuracy:** 0.993
-**auditor_senitment accuracy:** 0.974
+**Accuracies:**
+**financial_phrasebank accuracy:** 0.993\
+**auditor_senitment accuracy:** 0.974\
 
 ### Training Hyperparameters:
 
-**Learning Rate:** 2e-05
-**Train Batch Size:** 16
-**Eval Batch Size:** 16
-**Random Seed:** 42
-**Optimizer:** AdamW-betas(0.9, 0.999)
-**Learning Rate Scheduler:** Linear
-**Number of Epochs:** 6
-**Number of Warmup Steps:** 0.2 * Number of Training Steps
+**Learning Rate:** 2e-05\
+**Train Batch Size:** 16\
+**Eval Batch Size:** 16\
+**Random Seed:** 42\
+**Optimizer:** AdamW-betas(0.9, 0.999)\
+**Learning Rate Scheduler:** Linear\
+**Number of Epochs:** 6\
+**Number of Warmup Steps:** 0.2 * Number of Training Steps\
 
 ### How To Use:
 
 ```
->> from transformers import pipeline
->> pipe = pipeline("sentiment-analysis", model="mstafam/fine-tuned-bert-financial-sentimental-analysis")
+from transformers import pipeline
+pipe = pipeline("sentiment-analysis", model="mstafam/fine-tuned-bert-financial-sentimental-analysis")
 
->> text = "Example company has seen a 5% increase in revenue this quarter."
+text = "Example company has seen a 5% increase in revenue this quarter."
 
->> pipe(text)
+print(pipe(text))
 
 [{'label': 'Positive', 'score': 0.9993795156478882}]
 ```