Update README.md

README.md

@@ -8,42 +8,75 @@ metrics:
 model-index:
 - name: imdb-distilbert-funetuned
   results: []
+datasets:
+- ajaykarthick/imdb-movie-reviews
+language:
+- en
+library_name: transformers
+pipeline_tag: text-classification
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 should probably proofread and complete it, then remove this comment. -->
 
-# imdb-distilbert-funetuned
+To create a model card for the Hugging Face Hub based on your fine-tuned DistilBERT model for text classification on the IMDb dataset, here's a template you can use:
 
-This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
-It achieves the following results on the evaluation set:
-- Loss: 0.2319
-- Accuracy: 0.9320
+---
+
+# DistilBERT IMDb Sentiment Classifier
+
+## Model Description
+This is a fine-tuned version of [DistilBERT](https://huggingface.co/distilbert-base-uncased) for sentiment analysis on the IMDb movie review dataset. DistilBERT is a smaller, faster, and lighter variant of BERT, designed to perform efficiently while retaining the core strengths of BERT in natural language understanding.
+
+The model is trained to classify movie reviews as either **positive** or **negative** sentiments, making it ideal for applications where sentiment analysis is needed, such as analyzing customer feedback, social media posts, or reviews.
 
-## Model description
+## Intended Use
+This model is intended for text classification tasks, specifically sentiment analysis. It can be used to automatically label a piece of text as either having a positive or negative sentiment.
 
-More information needed
+### Use Cases
+- **Movie review sentiment analysis**
+- **Customer feedback analysis**
+- **Social media sentiment monitoring**
+- **Product review classification**
 
-## Intended uses & limitations
+## How to Use
 
-More information needed
+Here is how you can use this model with the Hugging Face `transformers` library:
 
-## Training and evaluation data
+```python
+from transformers import DistilBertTokenizer, DistilBertForSequenceClassification
+import torch
 
-More information needed
+# Load the model and tokenizer
+model_name = "Ashaduzzaman/imdb-distilbert-funetuned",
+tokenizer = DistilBertTokenizer.from_pretrained(model_name)
+model = DistilBertForSequenceClassification.from_pretrained(model_name)
 
-## Training procedure
+# Example text
+text = "The movie was absolutely fantastic! The acting was superb and the story was gripping."
 
-### Training hyperparameters
+# Tokenize and predict
+inputs = tokenizer(text, return_tensors="pt")
+outputs = model(**inputs)
+logits = outputs.logits
+predictions = torch.softmax(logits, dim=1)
 
-The following hyperparameters were used during training:
-- learning_rate: 2e-05
-- train_batch_size: 16
-- eval_batch_size: 16
-- seed: 42
-- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
-- lr_scheduler_type: linear
-- num_epochs: 2
+# Get the predicted label
+predicted_label = torch.argmax(predictions).item()
+labels = ["Negative", "Positive"]
+print(f"Predicted sentiment: {labels[predicted_label]}")
+```
+
+## Training Data
+This model was trained on the IMDb movie review dataset, a large dataset for binary sentiment classification. The dataset contains 50,000 highly polarized movie reviews. This dataset is balanced, with 25,000 positive and 25,000 negative reviews.
+
+## Training Procedure
+The model was fine-tuned using the IMDb dataset with the following configuration:
+- **Optimizer**: AdamW (Adam with betas=(0.9,0.999) and epsilon=1e-08)
+- **Learning Rate**: 2e-5
+- **Batch Size**: 16
+- **Epochs**: 2
+- **Max Sequence Length**: 512 tokens
 
 ### Training results
 
@@ -52,10 +85,25 @@ The following hyperparameters were used during training:
 | 0.2239        | 1.0   | 1563 | 0.2026          | 0.9227   |
 | 0.1468        | 2.0   | 3126 | 0.2319          | 0.9320   |
 
+- **Loss:** 0.2319
+- **Accuracy:** 0.9320
+  
+## Limitations
+- The model is specifically trained on the IMDb dataset, so its effectiveness may be reduced when applied to other domains or types of text.
+- Sentiment detection is binary (positive or negative). Neutral sentiments or more nuanced emotions are not captured.
+- The model may not perform well on text that is highly sarcastic, contains slang, or is very short (e.g., one-word reviews).
+
+## Ethical Considerations
+- **Bias**: The model may reflect biases present in the IMDb dataset. Users should be cautious about applying this model to sensitive applications.
+- **Content**: Since the IMDb dataset includes movie reviews, the model might not generalize well to text outside of this context.
+
+## Acknowledgments
+- The original [DistilBERT](https://huggingface.co/distilbert-base-uncased) model was developed by Hugging Face.
+- The IMDb dataset is provided by Stanford and can be found [here](https://ai.stanford.edu/~amaas/data/sentiment/).
 
-### Framework versions
+## Framework versions
 
 - Transformers 4.42.4
 - Pytorch 2.3.1+cu121
 - Datasets 2.21.0
-- Tokenizers 0.19.1
+- Tokenizers 0.19.1