picket-cliff
/

deepl-project-model

@@ -6,6 +6,9 @@ metrics:
 - f1
 base_model:
 - distilbert/distilbert-base-uncased
 ---
 # Model Card for Model ID
@@ -17,184 +20,65 @@ Text classification Model for Spam Detection  (Deep Learning Project).
 ### Model Description
-This
-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:** [More Information Needed]
 - **Model type:** Binary Text Classification
-- **Language(s) (NLP):** [More Information Needed]
 - **Finetuned from model:** DistilBERT
-### 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]
-### Out-of-Scope Use
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-[More Information Needed]
-## Bias, Risks, and Limitations
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-[More Information Needed]
-### 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.
-## How to Get Started with the Model
-Use the code below to get started with the model.
-[More Information Needed]
 ## 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. -->
-[More Information Needed]
-### Training Procedure
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-#### Preprocessing [optional]
-[More Information Needed]
-#### Training Hyperparameters
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-#### Speeds, Sizes, Times [optional]
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-[More Information Needed]
 ## Evaluation
-<!-- This section describes the evaluation protocols and provides the results. -->
 ### Testing Data, Factors & Metrics
 #### Testing Data
-<!-- This should link to a Dataset Card if possible. -->
-[More Information Needed]
-#### Factors
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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 #### Metrics
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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 ### Results
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 #### Summary
-## Model Examination [optional]
-<!-- Relevant interpretability work for the model goes here -->
-[More Information Needed]
-## Environmental Impact
-<!-- 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]
-### Model Architecture and Objective
-[More Information Needed]
-### Compute Infrastructure
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-#### Hardware
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-#### Software
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-## Citation [optional]
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-**BibTeX:**
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-**APA:**
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-## Glossary [optional]
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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-## More Information [optional]
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-## Model Card Authors [optional]
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-## Model Card Contact
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 - f1
 base_model:
 - distilbert/distilbert-base-uncased
+datasets:
+- AbdulHadi806/mail_spam_ham_dataset
+pipeline_tag: text-classification
 ---
 # Model Card for Model ID
 ### Model Description
+Model developped for the "Deep Learning with Python" course Project
+- **Developed by:** Diavila Rostaing Engandzi
 - **Model type:** Binary Text Classification
+- **Language(s) (NLP):** English
 - **Finetuned from model:** DistilBERT
+### Model Sources
 - **Repository:** [More Information Needed]
+- **Demo [optional]:** https://huggingface.co/picket-cliff/deepl-project
 ## Uses
+The model is intended to be used to sort spam in emails. Clone and Run the app.py file in the Demo to see it in action.
 ## Training Details
 ### Training Data
+Subset from the email_data.csv dataset [card].
+A benchmark dataset for email classification with around 5000 emailed classified between "ham" and "spam".
+To evaluate the model, data was separated between training and test datasets (80-20 split).
+#### Preprocessing
+Deep learning models cannot process raw text; they require numerical tensors. We utilized the Hugging Face DistilBertTokenizer.
+1.	Sub-word Tokenization: Instead of splitting by spaces (which struggles with typos and rare words), DistilBERT uses WordPiece tokenization. For example, an out-of-vocabulary word might be broken into known sub-words, preventing the model from encountering "Unknown" tokens.
+2.	Special Tokens: The tokenizer automatically prepends the [CLS] (Classification) token to the start of the sequence and the [SEP] (Separator) token at the end. The final hidden state corresponding to the [CLS] token is what the model uses for the binary classification decision.
+3.	Truncation and Padding: Transformer models require fixed-size input matrices for batch processing. Based on our EDA length distribution, we set max_length = 128.
+  o	Sentences longer than 128 tokens were truncated.
+  o	Sentences shorter than 128 tokens were padded with the [PAD] token (ID 0).
+4.	Attention Masks: To prevent the model from performing Self-Attention on meaningless padding tokens, the tokenizer generates an attention_mask (an array of 1s for real words and 0s for padding).
 ## Evaluation
+Results obtained directly from training on the training dataset then evaluating the model on the testing data.
+Result are compared to a baseline (dummy classifier) for reference.
 ### Testing Data, Factors & Metrics
 #### Testing Data
 #### Metrics
+Accuracy, f1 score (macro and weighted)
 ### Results
+When evaluated on a 80-20 split we obtained:
+•	Accuracy: 99.10%
+•	Macro Average F1-Score: 0.98
+•	Weighted Average F1-Score: 0.99
+Meanwhile the dummy achieved 86.6% accuracy.
 #### Summary
+The model performance is