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README.md

@@ -4,83 +4,49 @@ license: mit
 base_model: almanach/camembertav2-base
 tags:
 - generated_from_trainer
-- name
-- person
-- company
 metrics:
 - accuracy
 - precision
 - recall
 - f1
 model-index:
-- name: camembertav2-base-name-classifier-v2
+- name: camembertav2-base-name-classifier-v3
   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. -->
 
-# camembertav2-base-name-classifier-v2
+# camembertav2-base-name-classifier-v3
 
-This model is a fine-tuned version of [almanach/camembertav2-base](https://huggingface.co/almanach/camembertav2-base) on [ele-sage/person-company-names-classification](https://huggingface.co/datasets/ele-sage/person-company-names-classification) dataset.
+This model is a fine-tuned version of [almanach/camembertav2-base](https://huggingface.co/almanach/camembertav2-base) on an unknown dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.0318
-- Accuracy: 0.9915
+- Loss: 0.0260
+- Accuracy: 0.9931
 - Precision: 0.9967
-- Recall: 0.9883
-- F1: 0.9925
+- Recall: 0.9911
+- F1: 0.9939
 
 ## Model description
 
-This model is a high-performance binary text classifier, fine-tuned from `camembertav2-base`.
-Its purpose is to distinguish between a **person's name** and a **company/organization name** with high accuracy.
+More information needed
 
-### Direct Use
+## Intended uses & limitations
 
-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/camembertav2-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
-
-- 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).
-
-## Bias, Risks, and Limitations
-
-- **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 and evaluation data
 
+More information needed
 
 ## Training procedure
 
 ### Training hyperparameters
 
 The following hyperparameters were used during training:
-- learning_rate: 2e-05
-- train_batch_size: 256
-- eval_batch_size: 256
+- learning_rate: 1e-05
+- train_batch_size: 128
+- eval_batch_size: 128
 - 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
@@ -91,18 +57,30 @@ The following hyperparameters were used during training:
 
 | Training Loss | Epoch  | Step  | Validation Loss | Accuracy | Precision | Recall | F1     |
 |:-------------:|:------:|:-----:|:---------------:|:--------:|:---------:|:------:|:------:|
-| 0.0407        | 0.0826 | 2000  | 0.0346          | 0.9903   | 0.9939    | 0.9888 | 0.9914 |
-| 0.0325        | 0.1652 | 4000  | 0.0333          | 0.9908   | 0.9933    | 0.9903 | 0.9918 |
-| 0.0331        | 0.2479 | 6000  | 0.0314          | 0.9913   | 0.9962    | 0.9883 | 0.9923 |
-| 0.0345        | 0.3305 | 8000  | 0.0308          | 0.9917   | 0.9946    | 0.9906 | 0.9926 |
-| 0.0306        | 0.4131 | 10000 | 0.0318          | 0.9915   | 0.9967    | 0.9883 | 0.9925 |
-| 0.03          | 0.4957 | 12000 | 0.0294          | 0.9920   | 0.9954    | 0.9903 | 0.9929 |
-| 0.0301        | 0.5783 | 14000 | 0.0291          | 0.9920   | 0.9954    | 0.9904 | 0.9929 |
-| 0.0305        | 0.6609 | 16000 | 0.0282          | 0.9922   | 0.9960    | 0.9901 | 0.9930 |
-| 0.0282        | 0.7436 | 18000 | 0.0286          | 0.9922   | 0.9947    | 0.9914 | 0.9930 |
-| 0.0284        | 0.8262 | 20000 | 0.0284          | 0.9923   | 0.9957    | 0.9907 | 0.9932 |
-| 0.027         | 0.9088 | 22000 | 0.0281          | 0.9924   | 0.9959    | 0.9906 | 0.9932 |
-| 0.0289        | 0.9914 | 24000 | 0.0279          | 0.9924   | 0.9956    | 0.9909 | 0.9932 |
+| 0.0419        | 0.0411 | 2000  | 0.0383          | 0.9902   | 0.9954    | 0.9873 | 0.9913 |
+| 0.0333        | 0.0821 | 4000  | 0.0351          | 0.9912   | 0.9950    | 0.9895 | 0.9922 |
+| 0.0354        | 0.1232 | 6000  | 0.0340          | 0.9911   | 0.9975    | 0.9869 | 0.9921 |
+| 0.0316        | 0.1642 | 8000  | 0.0321          | 0.9918   | 0.9957    | 0.9897 | 0.9927 |
+| 0.0325        | 0.2053 | 10000 | 0.0299          | 0.9918   | 0.9947    | 0.9908 | 0.9928 |
+| 0.0304        | 0.2464 | 12000 | 0.0301          | 0.9920   | 0.9951    | 0.9908 | 0.9929 |
+| 0.0288        | 0.2874 | 14000 | 0.0301          | 0.9921   | 0.9959    | 0.9902 | 0.9930 |
+| 0.0329        | 0.3285 | 16000 | 0.0283          | 0.9923   | 0.9957    | 0.9907 | 0.9932 |
+| 0.0314        | 0.3696 | 18000 | 0.0276          | 0.9925   | 0.9960    | 0.9907 | 0.9933 |
+| 0.0277        | 0.4106 | 20000 | 0.0277          | 0.9926   | 0.9964    | 0.9905 | 0.9935 |
+| 0.0318        | 0.4517 | 22000 | 0.0279          | 0.9926   | 0.9968    | 0.9902 | 0.9935 |
+| 0.0246        | 0.4927 | 24000 | 0.0284          | 0.9927   | 0.9963    | 0.9908 | 0.9936 |
+| 0.0294        | 0.5338 | 26000 | 0.0276          | 0.9927   | 0.9966    | 0.9904 | 0.9935 |
+| 0.0304        | 0.5749 | 28000 | 0.0275          | 0.9925   | 0.9950    | 0.9918 | 0.9934 |
+| 0.0283        | 0.6159 | 30000 | 0.0268          | 0.9928   | 0.9968    | 0.9904 | 0.9936 |
+| 0.0304        | 0.6570 | 32000 | 0.0276          | 0.9928   | 0.9969    | 0.9904 | 0.9936 |
+| 0.0295        | 0.6981 | 34000 | 0.0274          | 0.9929   | 0.9963    | 0.9912 | 0.9937 |
+| 0.0266        | 0.7391 | 36000 | 0.0271          | 0.9930   | 0.9964    | 0.9912 | 0.9938 |
+| 0.0271        | 0.7802 | 38000 | 0.0270          | 0.9929   | 0.9971    | 0.9904 | 0.9937 |
+| 0.0277        | 0.8212 | 40000 | 0.0266          | 0.9930   | 0.9968    | 0.9909 | 0.9938 |
+| 0.027         | 0.8623 | 42000 | 0.0265          | 0.9931   | 0.9969    | 0.9908 | 0.9939 |
+| 0.0286        | 0.9034 | 44000 | 0.0262          | 0.9931   | 0.9966    | 0.9912 | 0.9939 |
+| 0.0275        | 0.9444 | 46000 | 0.0262          | 0.9931   | 0.9967    | 0.9912 | 0.9939 |
+| 0.0287        | 0.9855 | 48000 | 0.0260          | 0.9931   | 0.9967    | 0.9911 | 0.9939 |
 
 
 ### Framework versions
@@ -110,4 +88,4 @@ The following hyperparameters were used during training:
 - Transformers 4.57.1
 - Pytorch 2.9.0+cu128
 - Datasets 4.4.1
-- Tokenizers 0.22.1
+- Tokenizers 0.22.1

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