Update README.md

README.md

@@ -18,182 +18,100 @@ The HamOrSpamModel classifies messages as either spam
 
 The HamOrSpamModel is a text classification model designed to classify messages as either spam or non-spam (ham). It leverages the powerful Transformer architecture, fine-tuned on a labeled dataset of messages to achieve high accuracy in identifying spam messages. This model can be used in applications such as email filtering, SMS spam detection, and social media content moderation.
 - **Developed by:** [Ire Nkweke]
-- **Funded by [optional]:** [More Information Needed]
 - **Shared by [optional]:** [Ire Nkweke]
 - **Model type:** [Text Classification]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
 
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
 
 ## Uses
 
 <!-- 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
-
-<!-- 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]
+The HamOrSpamModel is designed to classify text messages as either spam or non-spam (ham). This model can be used in various applications including:
 
-### Out-of-Scope Use
+Email Filtering: Automatically classifying incoming emails to filter out spam and keep the inbox clean.
+SMS Spam Detection: Identifying and blocking spam messages sent to mobile phones.
+Social Media Content Moderation: Flagging spam content in social media posts and comments.
+Customer Support: Filtering spam messages from genuine customer inquiries in chatbots and support systems.
+Foreseeable users of the model include:
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+Developers: Integrating the model into applications for automated spam detection.
+Organizations: Implementing the model to protect users from spam messages in their communication platforms.
+Researchers: Analyzing the effectiveness of spam detection algorithms and improving upon them.
+Those affected by the model include:
 
-[More Information Needed]
+End-users: Benefiting from reduced spam in their communications.
+Spammers: Having their spam messages effectively blocked or flagged.
+Moderators: Receiving support in content moderation tasks.
 
 ## Bias, Risks, and Limitations
 
 <!-- This section is meant to convey both technical and sociotechnical limitations. -->
 
-[More Information Needed]
+The HamOrSpamModel has several limitations and potential biases:
+
+Data Bias: The model's training data may contain inherent biases, leading to biased predictions. For example, if the training data is skewed towards certain types of spam, the model might underperform on other types.
+False Positives and Negatives: The model might incorrectly classify legitimate messages as spam (false positives) or fail to identify spam messages (false negatives).
+Language Limitations: The model is primarily trained on English messages and might not perform well on messages in other languages.
+Context Understanding: The model may struggle with messages where the context determines whether they are spam or not (e.g., promotional messages from known contacts).
+Evolving Spam Techniques: Spammers continuously evolve their techniques to bypass spam filters, which might reduce the model's effectiveness over time.
 
 ### 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.
+Users (both direct and downstream) should be made aware of the risks, biases, and limitations of the model. More information needed for further recommendations.
+
+Regular Updates: Continuously update the model with new data to adapt to evolving spam techniques.
+Human Review: Implement a human-in-the-loop system where flagged messages are reviewed by humans to reduce false positives.
+Contextual Training: Fine-tune the model on domain-specific data to improve its performance in specific contexts (e.g., finance, healthcare).
+Bias Mitigation: Analyze the training data for biases and consider methods to mitigate them during model training.
 
 ## How to Get Started with the Model
 
 Use the code below to get started with the model.
 
-[More Information Needed]
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-## Training Details
-
-### Training Data
-
-<!-- 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. -->
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-[More Information Needed]
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-### Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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-#### Preprocessing [optional]
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-[More Information Needed]
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-#### Training Hyperparameters
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-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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-#### Speeds, Sizes, Times [optional]
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-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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-[More Information Needed]
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-## Evaluation
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-<!-- This section describes the evaluation protocols and provides the results. -->
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-### Testing Data, Factors & Metrics
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-#### Testing Data
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-<!-- This should link to a Dataset Card if possible. -->
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-[More Information Needed]
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-#### Factors
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-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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-[More Information Needed]
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-#### Metrics
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-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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-[More Information Needed]
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-### Results
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-[More Information Needed]
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-#### Summary
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-## Model Examination [optional]
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-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
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-## Environmental Impact
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-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
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-### Model Architecture and Objective
-
-[More Information Needed]
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-### Compute Infrastructure
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-[More Information Needed]
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-#### Hardware
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-[More Information Needed]
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-#### Software
+1. Install Dependencies:
 
-[More Information Needed]
+pip install transformers torch
 
-## Citation [optional]
+2.Load the Model and Tokenizer:
 
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
 
-**BibTeX:**
+model_name = "IreNkweke/HamOrSpamModel"
 
-[More Information Needed]
 
-**APA:**
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
 
-[More Information Needed]
+3. Classify Messages:
 
-## Glossary [optional]
+import torch
 
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+def classify_message(text):
+    inputs = tokenizer(text, padding=True, truncation=True, return_tensors="pt")
+    outputs = model(**inputs)
+    predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
+    return predictions
 
-[More Information Needed]
+message = "Congratulations! You've won a $1000 gift card. Click here to claim your prize."
+result = classify_message(message)
 
-## More Information [optional]
+print(f"Spam Probability: {result[0][1].item() * 100:.2f}%")
+print(f"Non-Spam Probability: {result[0][0].item() * 100:.2f}%")
 
-[More Information Needed]
+4. Example Usage:
+message = "You have a new message from John. Check it out!"
+result = classify_message(message)
 
-## Model Card Authors [optional]
+if result[0][1] > 0.5:
+    print("This message is spam.")
+else:
+    print("This message is not spam.")
 
-[More Information Needed]
+By following these steps, users can easily integrate the HamOrSpamModel into their applications to classify messages as spam or non-spam.
 
-## Model Card Contact
+Code Example
+Here is a simple code example to load and use the HamOrSpamModel:
 
-[More Information Needed]
+Install Dependencies: