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---
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
results: []
datasets:
- ele-sage/person-company-names-classification
language:
- fr
- en
new_version: ele-sage/mdeberta-v3-base-name-classifier-v2
---
# ⚠️ DEPRECATED MODEL ⚠️
**Please do not use this model for new projects.**
This model has been superseded by a newer, more accurate version trained on a larger, cleaner dataset.
It is maintained here for archival purposes only.
### ✅ Recommended Replacement:
Please switch to **[ele-sage/mdeberta-v3-base-name-classifier-v2](https://huggingface.co/ele-sage/mdeberta-v3-base-name-classifier-v2)** (Higher Accuracy).
---
# mdeberta-v3-base-name-classifier
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/datasets/ele-sage/person-company-names-classification) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0305
- Accuracy: 0.9922
- Precision: 0.9957
- Recall: 0.9906
- F1: 0.9931
## 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")
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: 8e-06
- train_batch_size: 64
- eval_batch_size: 64
- 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: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:------:|:-----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.0592 | 0.0203 | 2000 | 0.0526 | 0.9877 | 0.9912 | 0.9872 | 0.9892 |
| 0.0473 | 0.0406 | 4000 | 0.0429 | 0.9891 | 0.9940 | 0.9868 | 0.9904 |
| 0.0491 | 0.0610 | 6000 | 0.0407 | 0.9893 | 0.9949 | 0.9863 | 0.9906 |
| 0.0383 | 0.0813 | 8000 | 0.0386 | 0.9898 | 0.9954 | 0.9868 | 0.9911 |
| 0.0415 | 0.1016 | 10000 | 0.0378 | 0.9904 | 0.9950 | 0.9881 | 0.9915 |
| 0.0315 | 0.1219 | 12000 | 0.0410 | 0.9905 | 0.9955 | 0.9877 | 0.9916 |
| 0.0416 | 0.1422 | 14000 | 0.0387 | 0.9908 | 0.9950 | 0.9888 | 0.9919 |
| 0.0292 | 0.1625 | 16000 | 0.0383 | 0.9908 | 0.9964 | 0.9874 | 0.9919 |
| 0.0381 | 0.1829 | 18000 | 0.0357 | 0.9907 | 0.9959 | 0.9878 | 0.9918 |
| 0.0266 | 0.2032 | 20000 | 0.0395 | 0.9909 | 0.9938 | 0.9902 | 0.9920 |
| 0.035 | 0.2235 | 22000 | 0.0392 | 0.9909 | 0.9956 | 0.9885 | 0.9920 |
| 0.0333 | 0.2438 | 24000 | 0.0356 | 0.9910 | 0.9935 | 0.9907 | 0.9921 |
| 0.0321 | 0.2641 | 26000 | 0.0343 | 0.9909 | 0.9947 | 0.9894 | 0.9920 |
| 0.0308 | 0.2845 | 28000 | 0.0360 | 0.9912 | 0.9954 | 0.9892 | 0.9923 |
| 0.0317 | 0.3048 | 30000 | 0.0348 | 0.9912 | 0.9941 | 0.9905 | 0.9923 |
| 0.0359 | 0.3251 | 32000 | 0.0346 | 0.9913 | 0.9959 | 0.9889 | 0.9924 |
| 0.0437 | 0.3454 | 34000 | 0.0333 | 0.9912 | 0.9957 | 0.9889 | 0.9923 |
| 0.0401 | 0.3657 | 36000 | 0.0334 | 0.9914 | 0.9954 | 0.9895 | 0.9924 |
| 0.0419 | 0.3861 | 38000 | 0.0321 | 0.9915 | 0.9957 | 0.9895 | 0.9926 |
| 0.032 | 0.4064 | 40000 | 0.0339 | 0.9914 | 0.9947 | 0.9902 | 0.9925 |
| 0.0367 | 0.4267 | 42000 | 0.0314 | 0.9916 | 0.9948 | 0.9904 | 0.9926 |
| 0.0276 | 0.4470 | 44000 | 0.0355 | 0.9915 | 0.9954 | 0.9897 | 0.9925 |
| 0.0373 | 0.4673 | 46000 | 0.0321 | 0.9916 | 0.9954 | 0.9899 | 0.9926 |
| 0.0364 | 0.4876 | 48000 | 0.0327 | 0.9915 | 0.9966 | 0.9885 | 0.9925 |
| 0.0317 | 0.5080 | 50000 | 0.0311 | 0.9914 | 0.9934 | 0.9915 | 0.9924 |
| 0.0355 | 0.5283 | 52000 | 0.0307 | 0.9917 | 0.9957 | 0.9898 | 0.9927 |
| 0.0276 | 0.5486 | 54000 | 0.0321 | 0.9918 | 0.9952 | 0.9904 | 0.9928 |
| 0.0342 | 0.5689 | 56000 | 0.0319 | 0.9918 | 0.9956 | 0.9900 | 0.9928 |
| 0.0316 | 0.5892 | 58000 | 0.0314 | 0.9918 | 0.9949 | 0.9906 | 0.9928 |
| 0.0322 | 0.6096 | 60000 | 0.0315 | 0.9916 | 0.9942 | 0.9912 | 0.9927 |
| 0.0357 | 0.6299 | 62000 | 0.0309 | 0.9921 | 0.9955 | 0.9905 | 0.9930 |
| 0.0296 | 0.6502 | 64000 | 0.0326 | 0.9919 | 0.9955 | 0.9903 | 0.9929 |
| 0.0324 | 0.6705 | 66000 | 0.0312 | 0.9919 | 0.9958 | 0.9900 | 0.9929 |
| 0.0266 | 0.6908 | 68000 | 0.0319 | 0.9920 | 0.9958 | 0.9902 | 0.9930 |
| 0.028 | 0.7112 | 70000 | 0.0321 | 0.9920 | 0.9961 | 0.9899 | 0.9930 |
| 0.0276 | 0.7315 | 72000 | 0.0319 | 0.9919 | 0.9963 | 0.9895 | 0.9929 |
| 0.0288 | 0.7518 | 74000 | 0.0316 | 0.9920 | 0.9952 | 0.9908 | 0.9930 |
| 0.0295 | 0.7721 | 76000 | 0.0304 | 0.9920 | 0.9955 | 0.9904 | 0.9930 |
| 0.0305 | 0.7924 | 78000 | 0.0309 | 0.9920 | 0.9963 | 0.9896 | 0.9929 |
| 0.0298 | 0.8127 | 80000 | 0.0312 | 0.9921 | 0.9962 | 0.9899 | 0.9930 |
| 0.0241 | 0.8331 | 82000 | 0.0312 | 0.9921 | 0.9954 | 0.9907 | 0.9930 |
| 0.0332 | 0.8534 | 84000 | 0.0308 | 0.9920 | 0.9955 | 0.9906 | 0.9930 |
| 0.0281 | 0.8737 | 86000 | 0.0301 | 0.9922 | 0.9957 | 0.9905 | 0.9931 |
| 0.0274 | 0.8940 | 88000 | 0.0305 | 0.9921 | 0.9952 | 0.9908 | 0.9930 |
| 0.0263 | 0.9143 | 90000 | 0.0300 | 0.9922 | 0.9958 | 0.9905 | 0.9931 |
| 0.0215 | 0.9347 | 92000 | 0.0304 | 0.9921 | 0.9952 | 0.9909 | 0.9931 |
| 0.0367 | 0.9550 | 94000 | 0.0297 | 0.9922 | 0.9956 | 0.9907 | 0.9931 |
| 0.0298 | 0.9753 | 96000 | 0.0302 | 0.9922 | 0.9955 | 0.9908 | 0.9931 |
| 0.0202 | 0.9956 | 98000 | 0.0305 | 0.9922 | 0.9957 | 0.9906 | 0.9931 |
### Framework versions
- Transformers 4.57.1
- Pytorch 2.9.0+cu128
- Datasets 4.4.1
- Tokenizers 0.22.1 |