⚠️ 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This model has been superseded by a newer, more accurate version trained on a larger, cleaner dataset.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 (Higher Accuracy).

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mdeberta-v3-base-name-classifier

This model is a fine-tuned version of microsoft/mdeberta-v3-base on 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.

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

Framework versions

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