Update README.md

README.md

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 ---
 license: apache-2.0
+datasets:
+- dair-ai/emotion
+language:
+- en
+metrics:
+- accuracy
+- f1
+- precision
+- recall
+base_model:
+- albert/albert-large-v2
+pipeline_tag: text-classification
 ---
+# Sentiment classification using Albert-large-v2
+
+### Model Description
+
+This model is a fine-tuned version of the ALBERT-Large model designed for **emotion sentiment classification**. This model is capable of detecting six different emotional categories in text: **Anger**, **Disgust**, **Fear**, **Happiness**, **Sadness**, and **Surprise**. It achieves high performance on sentiment classification tasks, making it suitable for a variety of real-world applications such as emotion detection, content moderation, and sentiment analysis.
+
+## How to Get Started
+
+Use the code below to get started with the model.
+
+```python
+from transformers import pipeline
+
+emotion_classifier = pipeline("text-classification", model="SandeepVvigneshwar/sentiment-classification-albert-large-v2")
+
+text = "I am so happy to be part of this project!"
+emotion = emotion_classifier(text)
+print(emotion)
+```
+
+
+## Requirements
+
+- Python 3.x
+- Hugging Face `transformers` library
+- PyTorch or TensorFlow
+
+
+### Training Data
+
+[dair-ai/emotion](https://huggingface.co/datasets/dair-ai/emotion)
+
+#### Training Hyperparameters
+
+- learning_rate = 2e-5
+- per_device_train_batch_size = 8
+- per_device_eval_batch_size = 8
+- gradient_accumulation_steps = 2
+- num_train_epochs = 8
+- weight_decay = 0.01
+- fp16 = True
+- metric_for_best_model = "f1"
+- dataloader_num_workers = 4
+- max_grad_norm = 1.0
+- lr_scheduler_type = "linear"
+
+### Limits
+
+- Domain-specific Text: The model may not perform well on specialized or highly technical texts.
+- Languages: The model has been fine-tuned on English-language data and may not generalize well to other languages.
+- Input Length: The model performs best with shorter text inputs. For longer, more complex texts, performance may vary.
+
+## Evaluation
+
+| Metric                     | Value  |
+|----------------------------|--------|
+| **Evaluation Loss**        | 0.08795 |
+| **Evaluation Accuracy**    | 94.31%  |
+| **Evaluation F1-Score**    | 94.39%  |
+| **Evaluation Precision**   | 94.99%  |
+| **Evaluation Recall**      | 94.31%  |