README.md

---
library_name: transformers
license: mit
base_model: microsoft/mdeberta-v3-base
tags:
- generated_from_trainer
- name
- person
- company
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: mdeberta-v3-base-name-classifier-v2
  results: []
datasets:
- ele-sage/person-company-names-classification
language:
- fr
- en
---


# 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 [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: 0.0732
- Accuracy: 0.9946
- Precision: 0.9989
- Recall: 0.9913
- F1: 0.9951


## Model description

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

- 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.


## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 128
- eval_batch_size: 512
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 1
- label_smoothing_factor: 0.02

### Training results

| Training Loss | Epoch  | Step  | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:------:|:-----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.0914        | 0.0359 | 2000  | 0.0889          | 0.9907   | 0.9952    | 0.9882 | 0.9917 |
| 0.0796        | 0.0718 | 4000  | 0.0864          | 0.9907   | 0.9991    | 0.9843 | 0.9916 |
| 0.0808        | 0.1077 | 6000  | 0.0809          | 0.9919   | 0.9944    | 0.9910 | 0.9927 |
| 0.0828        | 0.1436 | 8000  | 0.0774          | 0.9930   | 0.9976    | 0.9899 | 0.9937 |
| 0.0787        | 0.1795 | 10000 | 0.0771          | 0.9931   | 0.9989    | 0.9886 | 0.9938 |
| 0.0761        | 0.2154 | 12000 | 0.0774          | 0.9935   | 0.9984    | 0.9899 | 0.9942 |
| 0.0779        | 0.2513 | 14000 | 0.0771          | 0.9935   | 0.9991    | 0.9892 | 0.9941 |
| 0.0833        | 0.2872 | 16000 | 0.0751          | 0.9937   | 0.9985    | 0.9903 | 0.9944 |
| 0.0812        | 0.3231 | 18000 | 0.0764          | 0.9935   | 0.9967    | 0.9915 | 0.9941 |
| 0.0763        | 0.3590 | 20000 | 0.0753          | 0.9940   | 0.9990    | 0.9902 | 0.9946 |
| 0.0753        | 0.3949 | 22000 | 0.0759          | 0.9936   | 0.9968    | 0.9917 | 0.9942 |
| 0.0749        | 0.4308 | 24000 | 0.0750          | 0.9940   | 0.9980    | 0.9912 | 0.9946 |
| 0.0755        | 0.4667 | 26000 | 0.0746          | 0.9939   | 0.9974    | 0.9917 | 0.9945 |
| 0.0755        | 0.5026 | 28000 | 0.0756          | 0.9937   | 0.9967    | 0.9919 | 0.9943 |
| 0.0753        | 0.5385 | 30000 | 0.0745          | 0.9942   | 0.9979    | 0.9916 | 0.9948 |
| 0.0791        | 0.5744 | 32000 | 0.0735          | 0.9943   | 0.9991    | 0.9908 | 0.9949 |
| 0.0789        | 0.6103 | 34000 | 0.0743          | 0.9939   | 0.9972    | 0.9918 | 0.9945 |
| 0.073         | 0.6462 | 36000 | 0.0741          | 0.9943   | 0.9985    | 0.9913 | 0.9949 |
| 0.0714        | 0.6821 | 38000 | 0.0738          | 0.9944   | 0.9989    | 0.9911 | 0.9950 |
| 0.0738        | 0.7180 | 40000 | 0.0733          | 0.9945   | 0.9989    | 0.9912 | 0.9950 |
| 0.0796        | 0.7539 | 42000 | 0.0732          | 0.9945   | 0.9987    | 0.9915 | 0.9951 |
| 0.0726        | 0.7898 | 44000 | 0.0734          | 0.9945   | 0.9988    | 0.9914 | 0.9951 |
| 0.0778        | 0.8257 | 46000 | 0.0733          | 0.9945   | 0.9988    | 0.9913 | 0.9951 |
| 0.0734        | 0.8616 | 48000 | 0.0733          | 0.9945   | 0.9989    | 0.9914 | 0.9951 |
| 0.0735        | 0.8975 | 50000 | 0.0732          | 0.9945   | 0.9988    | 0.9914 | 0.9951 |
| 0.0696        | 0.9334 | 52000 | 0.0732          | 0.9945   | 0.9989    | 0.9913 | 0.9951 |
| 0.0754        | 0.9693 | 54000 | 0.0732          | 0.9946   | 0.9989    | 0.9913 | 0.9951 |


### Framework versions

- Transformers 4.57.1
- Pytorch 2.9.0+cu128
- Datasets 4.4.1
- Tokenizers 0.22.1