Update README.md

README.md

@@ -1,6 +1,23 @@
 ---
 library_name: transformers
-tags: []
+tags:
+- text-generation-inference
+- spam-detection
+- nlp
+- binary-classification
+license: apache-2.0
+datasets:
+- bvk/SMS-spam
+language:
+- en
+metrics:
+- accuracy
+- f1
+- precision
+- recall
+base_model:
+- distilbert/distilbert-base-uncased
+pipeline_tag: text-classification
 ---
 
 # Model Card for Model ID
@@ -17,51 +34,117 @@ tags: []
 
 This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
 
-- **Developed by:** [Ainebyona Abubaker]
-- **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:** Ainebyona Abubaker
+- **Funded by :** This model was developed independenly by Ainebyona Abubaker with no external funding.
+- **Shared by :** Ainebyona Abubaker
+- **Model type:** DistilBERT
+- **Language(s) (NLP):** English
+- **License:** Apache 2.0 License
+- **Finetuned from model distilbert-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:** https://huggingface.co/kenbaker-gif/Email_Spam_Classifier
 
 ## Uses
 
+- This model can be used for:
+
+- Detecting spam messages in SMS or short text messages
+
+- Educational purposes in NLP and machine learning
+
+- Research and development of spam detection systems
+
 <!-- 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
 
+from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
+
+# Load the model and tokenizer
+model_name = "your-username/spam-classifier"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+
+# Create a text-classification pipeline
+classifier = pipeline("text-classification", model=model, tokenizer=tokenizer)
+
+# Example usage
+result = classifier("Congratulations! You've won a $500 gift card.")
+print(result)
+# Output: [{'label': 'SPAM', 'score': 0.99}]
+
+
 <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
 
-[More Information Needed]
 
-### Downstream Use [optional]
+
+### Downstream Use.
+- Email spam detection – fine-tune on email datasets for spam classification
+
+- Chat moderation – detecting unwanted or spammy messages in chat apps
+
+- SMS analytics – analyzing messaging patterns for marketing or user studies
+
+- Text classification pipelines – can be incorporated into larger NLP workflows
 
 <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
 
-[More Information Needed]
+
 
 ### Out-of-Scope Use
+- Not recommended for high-stakes decisions (legal, financial, or medical) without further validation
+
+- Performance on languages other than English is not guaranteed
+
+- Not tested on long-form text or other messaging platforms (email, social media)
 
 <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
 
-[More Information Needed]
+
 
 ## Bias, Risks, and Limitations
+Biases:
+
+- The model is trained on English SMS messages, so it may underperform on messages in other languages or dialects.
+
+- It may be biased toward patterns in the training data, such as certain spam phrases or formatting, which can lead to false positives or false negatives.
+
+- Minority or unusual types of spam may not be well recognized.
+
+Risks:
+
+- Misclassifying messages could lead to important messages being ignored or spam being delivered.
+
+- Using the model in high-stakes applications (legal, financial, medical) without proper validation could have serious consequences.
+
+Limitations:
+
+- Only trained for binary classification: HAM (not spam) vs SPAM.
+
+- Performance may degrade on longer texts, emails, or social media messages.
+
+- The model may need fine-tuning for datasets outside SMS messages to maintain accuracy.
 
 <!-- This section is meant to convey both technical and sociotechnical limitations. -->
 
 [More Information Needed]
 
 ### Recommendations
+- This model is recommended for detecting spam in short English text messages (SMS).
+
+- Suitable for educational, research, and prototype applications in NLP and text classification.
+
+- Not recommended for high-stakes environments (legal, financial, or medical) without further testing and validation.
+
+- Users are encouraged to fine-tune the model if applying it to new datasets, different languages, or longer text formats.
+
+- Always review model predictions before acting on them, especially in critical applications.
+
+💡 Tip:
 
 <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
 
@@ -71,18 +154,76 @@ Users (both direct and downstream) should be made aware of the risks, biases and
 
 Use the code below to get started with the model.
 
-[More Information Needed]
+from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
+
+# Load model and tokenizer
+model_name = "your-username/spam-classifier"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+
+# Create pipeline
+classifier = pipeline("text-classification", model=model, tokenizer=tokenizer)
+
+# Example usage
+result = classifier("Congratulations! You've won a $500 Amazon gift card.")
+print(result)
+# Output: [{'label': 'SPAM', 'score': 0.99}]
+
 
 ## Training Details
+- Base Model: distilbert-base-uncased (DistilBERT)
+
+- Task: Binary SMS spam classification (HAM / SPAM)
+
+- Dataset: SMS Spam Collection (80% train, 20% eval)
+
+- Preprocessing: Tokenized with padding & truncation
+
+- Training: 3 epochs, batch size 16, learning rate 2e-5, AdamW optimizer
+
+- Metrics: Accuracy, Weighted F1-score
+
+- Trained for short English SMS messages; fine-tuning may be needed for other text types or languages.
 
 ### Training Data
+- Primary Dataset: SMS Spam Collection Dataset
 
-<!-- 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. -->
+- Content: English SMS messages labeled as HAM (not spam) or SPAM
 
-[More Information Needed]
+- Size: ~5,500 messages
+
+- Preprocessing: Text tokenized with padding and truncation; labels mapped to 0 (HAM) and 1 (SPAM)
+
+- Additional Datasets: Optional — can combine with other SMS/spam datasets to improve generalization
+
+- The model is optimized for short English SMS messages; performance on other text types or languages may vary.
+
+<!-- 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. -->
 
 ### Training Procedure
+1. Data Preparation:
+     - Loaded the SMS Spam Collection dataset
+     - Tokenized messages using AutoTokenizer with padding and truncation
+     - Split dataset: 80% train, 20% evaluation
+
+2. Model Setup:
+     - Base model: distilbert-base-uncased
+     -Task: Binary classification (HAM vs SPAM)
+
+3. Training:
+     - Optimizer: AdamW
+     - Learning rate: 2e-5
+     - Batch size: 16 (train & eval)
+
+4. Number of epochs: 3
+
+5. Evaluation and checkpointing performed at each epoch.
+
+6. Metrics Monitored:
+    - Accuracy
+    - Weighted F1-score
 
+Training focused on short English SMS messages; additional fine-tuning may be needed for other datasets or text types.
 <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
 
 #### Preprocessing [optional]