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--- |
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library_name: transformers |
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license: mit |
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base_model: microsoft/mdeberta-v3-base |
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tags: |
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- generated_from_trainer |
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- name |
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- person |
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- company |
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metrics: |
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- accuracy |
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- precision |
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- recall |
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- f1 |
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model-index: |
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- name: mdeberta-v3-base-name-classifier |
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results: [] |
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datasets: |
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- ele-sage/person-company-names-classification |
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language: |
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- fr |
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- en |
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new_version: ele-sage/mdeberta-v3-base-name-classifier-v2 |
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--- |
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# ⚠️ DEPRECATED MODEL ⚠️ |
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**Please do not use this model for new projects.** |
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This model has been superseded by a newer, more accurate version trained on a larger, cleaner dataset. |
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It is maintained here for archival purposes only. |
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### ✅ Recommended Replacement: |
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Please switch to **[ele-sage/mdeberta-v3-base-name-classifier-v2](https://huggingface.co/ele-sage/mdeberta-v3-base-name-classifier-v2)** (Higher Accuracy). |
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--- |
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# mdeberta-v3-base-name-classifier |
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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. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0305 |
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- Accuracy: 0.9922 |
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- Precision: 0.9957 |
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- Recall: 0.9906 |
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- F1: 0.9931 |
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## Model description |
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This model is a high-performance binary text classifier, fine-tuned from `mdeberta-v3-base`. |
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Its purpose is to distinguish between a **person's name** and a **company/organization name** with high accuracy. |
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### Direct Use |
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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`. |
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```python |
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from transformers import pipeline |
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classifier = pipeline("text-classification", model="ele-sage/mdeberta-v3-base-name-classifier") |
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results = classifier([ |
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"Satya Nadella", |
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"Global Innovations Inc.", |
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"Martinez, Alonso" |
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]) |
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for result in results: |
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print(f"Text: '{result['text']}', Prediction: {result['label']}, Score: {result['score']:.4f}") |
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``` |
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### Downstream Use |
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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`. |
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### Out-of-Scope Use |
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- 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). |
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## Bias, Risks, and Limitations |
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- **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. |
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- **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. |
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- **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. |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 8e-06 |
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- train_batch_size: 64 |
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- eval_batch_size: 64 |
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- seed: 42 |
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- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 2000 |
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- num_epochs: 1 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |
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|:-------------:|:------:|:-----:|:---------------:|:--------:|:---------:|:------:|:------:| |
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| 0.0592 | 0.0203 | 2000 | 0.0526 | 0.9877 | 0.9912 | 0.9872 | 0.9892 | |
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| 0.0473 | 0.0406 | 4000 | 0.0429 | 0.9891 | 0.9940 | 0.9868 | 0.9904 | |
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| 0.0491 | 0.0610 | 6000 | 0.0407 | 0.9893 | 0.9949 | 0.9863 | 0.9906 | |
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| 0.0383 | 0.0813 | 8000 | 0.0386 | 0.9898 | 0.9954 | 0.9868 | 0.9911 | |
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| 0.0415 | 0.1016 | 10000 | 0.0378 | 0.9904 | 0.9950 | 0.9881 | 0.9915 | |
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| 0.0315 | 0.1219 | 12000 | 0.0410 | 0.9905 | 0.9955 | 0.9877 | 0.9916 | |
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| 0.0416 | 0.1422 | 14000 | 0.0387 | 0.9908 | 0.9950 | 0.9888 | 0.9919 | |
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| 0.0292 | 0.1625 | 16000 | 0.0383 | 0.9908 | 0.9964 | 0.9874 | 0.9919 | |
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| 0.0381 | 0.1829 | 18000 | 0.0357 | 0.9907 | 0.9959 | 0.9878 | 0.9918 | |
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| 0.0266 | 0.2032 | 20000 | 0.0395 | 0.9909 | 0.9938 | 0.9902 | 0.9920 | |
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| 0.035 | 0.2235 | 22000 | 0.0392 | 0.9909 | 0.9956 | 0.9885 | 0.9920 | |
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| 0.0333 | 0.2438 | 24000 | 0.0356 | 0.9910 | 0.9935 | 0.9907 | 0.9921 | |
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| 0.0321 | 0.2641 | 26000 | 0.0343 | 0.9909 | 0.9947 | 0.9894 | 0.9920 | |
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| 0.0308 | 0.2845 | 28000 | 0.0360 | 0.9912 | 0.9954 | 0.9892 | 0.9923 | |
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| 0.0317 | 0.3048 | 30000 | 0.0348 | 0.9912 | 0.9941 | 0.9905 | 0.9923 | |
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| 0.0359 | 0.3251 | 32000 | 0.0346 | 0.9913 | 0.9959 | 0.9889 | 0.9924 | |
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| 0.0437 | 0.3454 | 34000 | 0.0333 | 0.9912 | 0.9957 | 0.9889 | 0.9923 | |
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| 0.0401 | 0.3657 | 36000 | 0.0334 | 0.9914 | 0.9954 | 0.9895 | 0.9924 | |
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| 0.0419 | 0.3861 | 38000 | 0.0321 | 0.9915 | 0.9957 | 0.9895 | 0.9926 | |
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| 0.032 | 0.4064 | 40000 | 0.0339 | 0.9914 | 0.9947 | 0.9902 | 0.9925 | |
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| 0.0367 | 0.4267 | 42000 | 0.0314 | 0.9916 | 0.9948 | 0.9904 | 0.9926 | |
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| 0.0276 | 0.4470 | 44000 | 0.0355 | 0.9915 | 0.9954 | 0.9897 | 0.9925 | |
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| 0.0373 | 0.4673 | 46000 | 0.0321 | 0.9916 | 0.9954 | 0.9899 | 0.9926 | |
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| 0.0364 | 0.4876 | 48000 | 0.0327 | 0.9915 | 0.9966 | 0.9885 | 0.9925 | |
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| 0.0317 | 0.5080 | 50000 | 0.0311 | 0.9914 | 0.9934 | 0.9915 | 0.9924 | |
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| 0.0355 | 0.5283 | 52000 | 0.0307 | 0.9917 | 0.9957 | 0.9898 | 0.9927 | |
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| 0.0276 | 0.5486 | 54000 | 0.0321 | 0.9918 | 0.9952 | 0.9904 | 0.9928 | |
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| 0.0342 | 0.5689 | 56000 | 0.0319 | 0.9918 | 0.9956 | 0.9900 | 0.9928 | |
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| 0.0316 | 0.5892 | 58000 | 0.0314 | 0.9918 | 0.9949 | 0.9906 | 0.9928 | |
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| 0.0322 | 0.6096 | 60000 | 0.0315 | 0.9916 | 0.9942 | 0.9912 | 0.9927 | |
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| 0.0357 | 0.6299 | 62000 | 0.0309 | 0.9921 | 0.9955 | 0.9905 | 0.9930 | |
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| 0.0296 | 0.6502 | 64000 | 0.0326 | 0.9919 | 0.9955 | 0.9903 | 0.9929 | |
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| 0.0324 | 0.6705 | 66000 | 0.0312 | 0.9919 | 0.9958 | 0.9900 | 0.9929 | |
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| 0.0266 | 0.6908 | 68000 | 0.0319 | 0.9920 | 0.9958 | 0.9902 | 0.9930 | |
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| 0.028 | 0.7112 | 70000 | 0.0321 | 0.9920 | 0.9961 | 0.9899 | 0.9930 | |
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| 0.0276 | 0.7315 | 72000 | 0.0319 | 0.9919 | 0.9963 | 0.9895 | 0.9929 | |
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| 0.0288 | 0.7518 | 74000 | 0.0316 | 0.9920 | 0.9952 | 0.9908 | 0.9930 | |
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| 0.0295 | 0.7721 | 76000 | 0.0304 | 0.9920 | 0.9955 | 0.9904 | 0.9930 | |
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| 0.0305 | 0.7924 | 78000 | 0.0309 | 0.9920 | 0.9963 | 0.9896 | 0.9929 | |
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| 0.0298 | 0.8127 | 80000 | 0.0312 | 0.9921 | 0.9962 | 0.9899 | 0.9930 | |
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| 0.0241 | 0.8331 | 82000 | 0.0312 | 0.9921 | 0.9954 | 0.9907 | 0.9930 | |
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| 0.0332 | 0.8534 | 84000 | 0.0308 | 0.9920 | 0.9955 | 0.9906 | 0.9930 | |
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| 0.0281 | 0.8737 | 86000 | 0.0301 | 0.9922 | 0.9957 | 0.9905 | 0.9931 | |
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| 0.0274 | 0.8940 | 88000 | 0.0305 | 0.9921 | 0.9952 | 0.9908 | 0.9930 | |
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| 0.0263 | 0.9143 | 90000 | 0.0300 | 0.9922 | 0.9958 | 0.9905 | 0.9931 | |
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| 0.0215 | 0.9347 | 92000 | 0.0304 | 0.9921 | 0.9952 | 0.9909 | 0.9931 | |
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| 0.0367 | 0.9550 | 94000 | 0.0297 | 0.9922 | 0.9956 | 0.9907 | 0.9931 | |
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| 0.0298 | 0.9753 | 96000 | 0.0302 | 0.9922 | 0.9955 | 0.9908 | 0.9931 | |
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| 0.0202 | 0.9956 | 98000 | 0.0305 | 0.9922 | 0.9957 | 0.9906 | 0.9931 | |
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### Framework versions |
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- Transformers 4.57.1 |
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- Pytorch 2.9.0+cu128 |
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- Datasets 4.4.1 |
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- Tokenizers 0.22.1 |
