Update README.md

README.md

@@ -1,118 +1,140 @@
----
-library_name: transformers
-tags: []
----
-
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
-
+---
+library_name: transformers
+tags:
+- sentiment-analysis
+- bert
+- fine-tuned-model
+- NLP
+license: apache-2.0
+language:
+- en
+base_model:
+- google-bert/bert-base-uncased
+datasets:
+- adilbekovich/Sentiment140Twitter
+---
+
+# Model Card for SentimentBERT
+
+This model is a fine-tuned version of `bert-base-uncased` for sentiment analysis. It has been trained on the **Sentiment140 Kaggle dataset**, enabling it to classify text as **positive** or **negative**.
 
 ## Model Details
 
 ### Model Description
 
-<!-- Provide a longer summary of what this model is. -->
-
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+This model is fine-tuned using the `bert-base-uncased` architecture to perform sentiment analysis. It accepts text input and predicts whether the sentiment expressed in the text is positive or negative.
 
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
+- **Developed by:** Debopam(Pritam) Dey
+- **Funded by [optional]:** Not specified
+- **Shared by [optional]:** Debopam(Pritam) Dey
+- **Model type:** Sequence classification (binary sentiment analysis)
+- **Language(s) (NLP):** English
+- **License:** Apache 2.0
+- **Finetuned from model [optional]:** bert-base-uncased
 
 ### Model Sources [optional]
 
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+- **Repository:** [SentimentBERT](https://huggingface.co/pritam2014/SentimentBERT)
+- **Demo [optional]:** Coming Soon
 
 ## Uses
-```
-from transformers import BertTokenizer, BertForSequenceClassification
+
+Here’s how to use the model for sentiment analysis:
+
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
 
 # Load the model and tokenizer from the Hugging Face model hub
-mymodel = BertForSequenceClassification.from_pretrained("pritam2014/SentimentBERT")
-mytokenizer = BertTokenizer.from_pretrained("pritam2014/SentimentBERT",use_auth_token=True)
-```
-```
+mymodel = AutoModelForSequenceClassification.from_pretrained("pritam2014/SentimentBERT")
+mytokenizer = AutoTokenizer.from_pretrained("pritam2014/SentimentBERT")
+
+# Preprocess the text input
 def preprocess_text(text):
-    # Preprocess the input text
     inputs = mytokenizer.encode_plus(
         text,
-        max_length=512,
+        max_length=50,
         padding='max_length',
         truncation=True,
         return_attention_mask=True,
         return_tensors='pt'
     )
     return inputs
-```
-```
+
+# Predict sentiment
 def make_prediction(text):
-    # Preprocess the input text
     inputs = preprocess_text(text)
-
-    # Make predictions using the loaded model
     with torch.no_grad():
         outputs = mymodel(inputs['input_ids'], attention_mask=inputs['attention_mask'])
         logits = outputs.logits
         predicted_class_id = torch.argmax(logits).item()
-
-    # Map the predicted class ID to a sentiment label
     sentiment_labels = {0: 'Negative', 1: 'Positive'}
-    predicted_sentiment = sentiment_labels[predicted_class_id]
+    return sentiment_labels[predicted_class_id]
 
-    return predicted_sentiment
-```
-```
-text = "I love this product"
-predicted_sentiment = make_prediction(text)
-print(predicted_sentiment)
+# Example
+text = "I love this product!"
+print(make_prediction(text))  # Output: Positive
 ```
 <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
 
 ### Direct Use
+The model can be used for text classification tasks without additional fine-tuning.
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+tokenizer = AutoTokenizer.from_pretrained("pritam2014/SentimentBERT")
+model = AutoModelForSequenceClassification.from_pretrained("pritam2014/SentimentBERT")
+
+from transformers import pipeline
+
+# Initialize pipeline
+sentiment_pipeline = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer)
+
+# Example input
+tweets = [
+    "I love this product!", 
+    "I'm not happy with the service.", 
+    "It's okay, could be better."
+]
 
+# Predict sentiment
+results = sentiment_pipeline(tweets)
+for tweet, result in zip(tweets, results):
+    print(f"Tweet: {tweet}\nSentiment: {result['label']}, Score: {result['score']:.4f}\n")
+```
 <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
 
-[More Information Needed]
 
 ### Downstream Use [optional]
 
 <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
 
-[More Information Needed]
+Users can fine-tune the model on other sentiment datasets or adapt it for related tasks like emotion detection.
 
 ### Out-of-Scope Use
 
 <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
 
-[More Information Needed]
+The model is not suitable for multilingual sentiment analysis or highly nuanced text where sentiment depends on complex context.
 
 ## Bias, Risks, and Limitations
 
 <!-- This section is meant to convey both technical and sociotechnical limitations. -->
 
-[More Information Needed]
+- The model may inherit biases present in the Sentiment140 dataset.
+- It is designed for English text and may perform poorly on non-English or mixed-language text.
+
 
 ### Recommendations
 
 <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
 
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+Use the model in scenarios where binary sentiment classification is sufficient. Avoid deploying it in critical systems without further testing for biases and limitations.
 
 ## How to Get Started with the Model
 
-Use the code below to get started with the model.
+Refer to the "Uses" section above to see the sample usage code. For more details, visit the Hugging Face Hub page.
 
-[More Information Needed]
 
 ## Training Details
 
@@ -120,11 +142,16 @@ Use the code below to get started with the model.
 
 <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
 
-[More Information Needed]
+The model was fine-tuned on the Sentiment140 dataset, which contains 1.6 million tweets labelled as positive or negative.
 
 ### Training Procedure
 
 <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+- Optimizer: AdamW
+- Batch size: 760
+- Learning rate: 1e-5
+- Epochs: 2
+- Hardware: Kaggle T4 GPU
 
 #### Preprocessing [optional]
 
@@ -144,6 +171,7 @@ Use the code below to get started with the model.
 ## Evaluation
 
 <!-- This section describes the evaluation protocols and provides the results. -->
+The model was evaluated on a validation split of the Sentiment140 dataset.
 
 ### Testing Data, Factors & Metrics
 
@@ -187,7 +215,7 @@ Carbon emissions can be estimated using the [Machine Learning Impact calculator]
 
 - **Hardware Type:** [More Information Needed]
 - **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
+- **Cloud Provider:** Kaggle T4 GPU
 - **Compute Region:** [More Information Needed]
 - **Carbon Emitted:** [More Information Needed]
 
@@ -215,7 +243,14 @@ Carbon emissions can be estimated using the [Machine Learning Impact calculator]
 
 **BibTeX:**
 
-[More Information Needed]
+@misc{pritam2014SentimentBERT,
+  author = {Debopam(Pritam) Dey},
+  title = {SentimentBERT},
+  year = {2025},
+  publisher = {Hugging Face},
+  howpublished = {\url{https://huggingface.co/pritam2014/SentimentBERT}},
+}
+
 
 **APA:**
 
@@ -229,12 +264,12 @@ Carbon emissions can be estimated using the [Machine Learning Impact calculator]
 
 ## More Information [optional]
 
-[More Information Needed]
+The model performs well on short texts like tweets but may require further fine-tuning for longer or domain-specific text.
 
 ## Model Card Authors [optional]
 
-[More Information Needed]
+ [More Information Needed] 
 
 ## Model Card Contact
 
-[More Information Needed]
+For questions or feedback, feel free to contact me via the Hugging Face repository or email at ([email protected])