Update README.md

README.md

@@ -4,6 +4,9 @@ license: mit
 base_model: microsoft/mdeberta-v3-base
 tags:
 - generated_from_trainer
+- name
+- person
+- company
 metrics:
 - accuracy
 - precision
@@ -12,14 +15,19 @@ metrics:
 model-index:
 - name: mdeberta-v3-base-name-classifier-v2
   results: []
+datasets:
+- ele-sage/person-company-names-classification
+language:
+- fr
+- en
 ---
 
-<!-- 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. -->
 
 # mdeberta-v3-base-name-classifier-v2
 
-This model is a fine-tuned version of [microsoft/mdeberta-v3-base](https://huggingface.co/microsoft/mdeberta-v3-base) on an unknown dataset.
+This model is a fine-tuned version of [microsoft/mdeberta-v3-base](https://huggingface.co/microsoft/mdeberta-v3-base) on [ele-sage/person-company-names-classification](https://huggingface.co/ele-sage/person-company-names-classification).
+
+
 It achieves the following results on the evaluation set:
 - Loss: 0.0215
 - Accuracy: 0.9943
@@ -29,15 +37,42 @@ It achieves the following results on the evaluation set:
 
 ## Model description
 
-More information needed
+This model is a high-performance binary text classifier, fine-tuned from `mdeberta-v3-base`.
+Its purpose is to distinguish between a **person's name** and a **company/organization name** with high accuracy.
+
+### Direct Use
+
+This model is intended to be used for text classification. Given a string, it will return a label indicating whether the string is a `Person` or a `Company`.
+
+```python
+from transformers import pipeline
+
+classifier = pipeline("text-classification", model="ele-sage/mdeberta-v3-base-name-classifier-v2")
+
+results = classifier([
+    "Satya Nadella",
+    "Global Innovations Inc.",
+    "Martinez, Alonso"
+])
+
+for result in results:
+    print(f"Text: '{result['text']}', Prediction: {result['label']}, Score: {result['score']:.4f}")
+```
+
+### Downstream Use
+
+This model is a key component of a two-stage name processing pipeline. It is designed to be used as a fast, efficient "gatekeeper" to first identify person names before passing them to a more complex parsing model, such as `ele-sage/distilbert-base-uncased-name-splitter`.
+
+### Out-of-Scope Use
 
-## Intended uses & limitations
+- This model is not a general-purpose classifier. It is highly specialized for distinguishing persons from companies and will not perform well on other classification tasks (e.g., sentiment analysis).
 
-More information needed
+## Bias, Risks, and Limitations
 
-## Training and evaluation data
+- **Geographic & Cultural Bias:** The training data is heavily biased towards North American (Canadian) person names and Quebec-based company names. The model will be less accurate when classifying names from other cultural or geographic origins.
+- **Ambiguity:** Certain names can legitimately be both a person's name and a company's name (e.g., "Ford"). In these cases, the model makes a statistical guess based on its training data, which may not always align with the specific context.
+- **Data Source:** The person name data is derived from a Facebook data leak and contains noise. While a rigorous cleaning process was applied, the model may have learned from some spurious data.
 
-More information needed
 
 ## Training procedure