Update README.md

README.md

@@ -3,61 +3,133 @@ library_name: transformers
 license: mit
 base_model: Davlan/afro-xlmr-base
 tags:
-- generated_from_trainer
+- amharic
+- text-classification
+- sentiment-analysis
+- intent-classification
+- code-switching
+- ethiopia
+- adfluence-ai
 metrics:
 - f1
 model-index:
-- name: adfluence-intent-model
-  results: []
+- name: Adfluence-AI Purchase Intent Classifier
+  results:
+  - task:
+      type: text-classification
+    dataset:
+      name: YosefA/Adflufence-ad-comments
+      type: YosefA/Adflufence-ad-comments
+      config: default
+      split: test
+    metrics:
+    - name: F1 (Weighted)
+      type: f1
+      value: 0.8101
+datasets:
+- YosefA/Adflufence-ad-comments
+language:
+- am
+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. -->
+# Adfluence-AI Purchase Intent Classifier
 
-# adfluence-intent-model
+This model is a fine-tuned version of **[Davlan/afro-xlmr-base](https://huggingface.co/Davlan/afro-xlmr-base)**, a powerful multilingual model with a strong understanding of African languages. It has been specifically trained to classify purchase intent in social media comments written in Amharic (Ge'ez script), Romanized Amharic, and mixed Amharic-English (code-switching).
 
-This model is a fine-tuned version of [Davlan/afro-xlmr-base](https://huggingface.co/Davlan/afro-xlmr-base) on an unknown dataset.
-It achieves the following results on the evaluation set:
-- Loss: 0.4281
-- F1: 0.8063
+This model was developed for the **Adfluence AI** project, which aims to evaluate the effectiveness of influencer marketing campaigns in Ethiopia.
 
-## Model description
+It achieves a **weighted F1-score of 0.81** on the evaluation set.
 
-More information needed
+## Model Description
 
-## Intended uses & limitations
+The model takes a social media comment as input and outputs a prediction across five categories of purchase intent:
 
-More information needed
+*   `highly_likely`
+*   `likely`
+*   `neutral`
+*   `unlikely`
+*   `highly_unlikely`
 
-## Training and evaluation data
+This allows for a nuanced understanding of audience reaction beyond simple positive/negative sentiment, directly measuring the potential for user conversion.
 
-More information needed
+## How to Use
 
-## Training procedure
+You can use this model directly with the `pipeline` function from the `transformers` library.
 
-### Training hyperparameters
+```python
+from transformers import pipeline
 
-The following hyperparameters were used during training:
-- learning_rate: 2e-05
-- train_batch_size: 16
-- eval_batch_size: 16
-- seed: 42
-- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
-- lr_scheduler_type: linear
-- num_epochs: 3
+# Load the model from the Hub
+model_id = "your-username/adfluence-intent-model" #<-- Replace with your model ID
+classifier = pipeline("text-classification", model=model_id)
+
+# --- Example Usage ---
+
+# Example 1: Amharic (Ge'ez Script) - Clear intent
+comment_1 = "ዋው በጣም አሪፍ ነው! የት ነው ማግኘት የምችለው?" 
+# Translation: "Wow, this is great! Where can I find it?"
 
-### Training results
+# Example 2: Mixed Amharic-English - Neutral/Questioning
+comment_2 = "Hmm, interesting. Price-u endet new?"
+# Translation: "Hmm, interesting. How is the price?"
 
-| Training Loss | Epoch | Step | Validation Loss | F1     |
-|:-------------:|:-----:|:----:|:---------------:|:------:|
-| No log        | 1.0   | 160  | 0.5001          | 0.7852 |
-| No log        | 2.0   | 320  | 0.4316          | 0.8101 |
-| No log        | 3.0   | 480  | 0.4281          | 0.8063 |
+# Example 3: Romanized Amharic - Negative
+comment_3 = "Ene enja minim altemechegnim, quality yelelew neger new."
+# Translation: "I don't know, I didn't like it at all, it's a thing with no quality."
 
 
-### Framework versions
+results = classifier([comment_1, comment_2, comment_3])
+for comment, result in zip([comment_1, comment_2, comment_3], results):
+    print(f"Comment: {comment}")
+    print(f"Prediction: {result['label']}, Score: {result['score']:.4f}\n")
 
-- Transformers 4.52.4
-- Pytorch 2.6.0+cu124
-- Datasets 3.6.0
-- Tokenizers 0.21.1
+# Expected Output:
+# Comment: ዋው በጣም አሪፍ ነው! የት ነው ማግኘት የምችለው?
+# Prediction: highly_likely, Score: 0.9851
+#
+# Comment: Hmm, interesting. Price-u endet new?
+# Prediction: neutral, Score: 0.9214
+#
+# Comment: Ene enja minim altemechegnim, quality yelelew neger new.
+# Prediction: highly_unlikely, Score: 0.9902
+
+Intended Uses & Limitations
+Intended Use
+This model is intended to be used as a backend component for the Adfluence AI platform. Its primary purpose is to analyze user comments on social media advertisements (e.g., on Instagram, Facebook, TikTok) to gauge audience purchase intent and provide campaign performance metrics.
+Limitations
+Simulated Data: The model is trained on a high-quality simulated dataset, not on live social media data. While designed to reflect real-world usage, performance may vary on wild, un-sanitized data.
+Domain Specificity: The source data was derived from product reviews (specifically for electronics). The model's performance may be strongest in the e-commerce/product domain and might require further fine-tuning for vastly different domains like services, events, or fashion.
+Language Scope: The model only understands Amharic and English. It has not been trained on other Ethiopian languages like Tigrinya, Oromo, etc.
+Training and Evaluation Data
+This model was fine-tuned on the custom YosefA/Adflufence-ad-comments dataset.
+The dataset was created through the following process:
+Source: Started with ~5,000 English product reviews from an Amazon dataset.
+Transformation: Each review was programmatically rephrased and translated into a simulated social media comment using Google's Gemini Flash.
+Stylization: Comments were generated in three styles to mimic real-world Ethiopian user behavior:
+Amharic (Ge’ez script)
+Romanized Amharic
+Mixed Amharic-English (Code-Switching)
+Enrichment: Comments were styled with emojis, slang, and informal sentence structures.
+Labeling: Each comment was assigned a purchase intent label mapped from the original star rating of the source review.
+Training Procedure
+Training Hyperparameters
+The following hyperparameters were used during training:
+learning_rate: 2e-05
+train_batch_size: 16
+eval_batch_size: 16
+seed: 42
+optimizer: AdamW with betas=(0.9,0.999) and epsilon=1e-08
+lr_scheduler_type: linear
+num_epochs: 3
+Training Results
+The model achieved its best performance at the end of Epoch 2.
+Training Loss	Epoch	Step	Validation Loss	F1 (Weighted)
+No log	1.0	160	0.5001	0.7852
+No log	2.0	320	0.4316	0.8101
+No log	3.0	480	0.4281	0.8063
+Framework Versions
+Transformers 4.41.2
+Pytorch 2.3.0+cu121
+Datasets 2.19.0
+Tokenizers 0.19.1