tferhan
/

Intent-GovMa-v1

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 ---
 library_name: transformers
-tags: []
 ---
 # Model Card for Model ID
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 ## Model Details
@@ -15,185 +30,103 @@ tags: []
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-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]
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 ## How to Get Started with the Model
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 ---
 library_name: transformers
+tags:
+- open data
+- morocco
+- questions
+- intents
+- classification
+- function calling
+license: apache-2.0
+language:
+- fr
+metrics:
+- accuracy
+pipeline_tag: text-classification
 ---
 # Model Card for Model ID
 <!-- Provide a quick summary of what the model is/does. -->
+This model is fine-tuned from the `camembert-base` model and is designed to classify user intent
+questions for the website data.gov.ma in French. It can distinguish whether a user is making a general inquiry
+or requesting specific data. The training data was generated using GPT-4o-mini and includes information specific
+to data.gov.ma. The model was fine-tuned using LoRA with specific hyperparameters, achieving an accuracy of up to 0.98.
 ## Model Details
 <!-- Provide a longer summary of what this model is. -->
+- **Developed by:** TFERHAN
+- **Language(s) (NLP):** French
+- **License:** Apache 2.0
+- **Finetuned from model [optional]:** camembert-base
+## Use Case
+- **Purpose:** Classify user intent questions for the chatbot on the data.gov.ma website.
+- **Languages:** French (optimized for), performs poorly on other languages.
+- **Data Source:** Generated using GPT-4o-mini with data from data.gov.ma.
 ## Uses
 ### Direct Use
+The model can be directly used to classify user intents in chatbot scenarios for the website data.gov.ma, distinguishing between general inquiries and data requests.
+### Downstream Use
+The model is particularly suited for applications involving the French language and can be integrated into larger chatbot systems or
+fine-tuned further for similar tasks in different contexts.
 ### Out-of-Scope Use
+- Misuse for different languages without fine-tuning.
+- Applications that do not involve French language queries.
+- Sensitive or highly critical applications without extensive validation.
 ## Bias, Risks, and Limitations
+### Technical Limitations
+- Performance may degrade significantly on languages other than French.
+- Limited to intents related to general queries and data requests.
 ### Recommendations
+- The model should be retrained or fine-tuned with appropriate data before deployment in non-French contexts.
+- Continuous monitoring and evaluation should be conducted to ensure reliability and fairness.
 ## How to Get Started with the Model
+Use the code snippet below to get started with the model:
+```python
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+import torch
+model_name = "tferhan/Intent-GovMa-v1"
+# Load the tokenizer and model
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+# Example inference
+questions = ["qu'est ce que open data",
+           "je veux les informations de l'eau potable"]
+for question in questions:
+        inputs = tokenizer(question, return_tensors="pt", padding=True, truncation=True, max_length=512).to(device)
+        with torch.no_grad():
+            outputs = model(**inputs)
+        print(f"question: {question}")
+        print(f"predicted type of question: {class_names[torch.argmax(outputs.logits).item()]}\n")
+```
+## Training Details
+### Training Data
+- **Data Source:** Generated using GPT-4o-mini with help from words and data from data.gov.ma.
+### Training Procedure
+- **Preprocessing:**
+  - Standard text preprocessing steps - tokenization, text cleaning, and normalization.
+- **Training Hyperparameters:**
+  - Epochs: `10`
+  - Train Batch Size: `4`
+  - Eval Batch Size: `4`
+  - Learning Rate: `2e-5`
+  - Evaluation Strategy: `epoch`
+  - Weight Decay: `0.01`
+- **Log History:** `log_history.json`
 ## Evaluation
+### Testing Data & Metrics
+- **Testing Data:** Subset of the generated data based on data.gov.ma.
+- **Evaluation Metrics:** Accuracy.
 ### Results
+- **Maximum Accuracy:** 0.98%