Update README.md

README.md

@@ -1,3 +1,40 @@
 ---
 license: mit
+tags:
+- sentiment
+- sentiment-analysis
+- financial
+- fine-tuned
+- fine-tuned-bert
+- bert-uncased
 ---
+
+### 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:
+
+**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
+
+### How To Use:
+
+```
+>> 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."
+
+>> pipe(text)
+
+[{'label': 'Positive', 'score': 0.9993795156478882}]
+```