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mdeberta-v3-base-name-classifier-v2

Text Classification
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deberta-v2
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name
person
company
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ele-sage/mdeberta-v3-base-name-classifier-v2

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  1. README.md +42 -73
  2. model.safetensors +1 -1
  3. runs/Dec07_13-56-05_elesage-pc/events.out.tfevents.1765133854.elesage-pc.82189.0 +3 -0
  4. runs/Dec07_15-25-19_elesage-pc/events.out.tfevents.1765139207.elesage-pc.107385.0 +3 -0
  5. training_args.bin +1 -1
README.md CHANGED
@@ -4,9 +4,6 @@ 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
@@ -15,63 +12,32 @@ metrics:
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  model-index:
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  - name: mdeberta-v3-base-name-classifier-v2
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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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  ---
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- # mdeberta-v3-base-name-classifier-v2
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-
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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/ele-sage/person-company-names-classification).
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  It achieves the following results on the evaluation set:
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- - Loss: 0.0269
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- - Accuracy: 0.9927
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- - Precision: 0.9966
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- - Recall: 0.9906
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- - F1: 0.9936
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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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-
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- ### Direct Use
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-
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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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-
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- ```python
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- from transformers import pipeline
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-
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- classifier = pipeline("text-classification", model="ele-sage/mdeberta-v3-base-name-classifier-v2")
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-
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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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-
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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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-
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- ### Downstream Use
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-
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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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-
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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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@@ -91,30 +57,33 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
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  |:-------------:|:------:|:-----:|:---------------:|:--------:|:---------:|:------:|:------:|
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- | 0.0495 | 0.0411 | 2000 | 0.0448 | 0.9881 | 0.9963 | 0.9826 | 0.9894 |
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- | 0.0433 | 0.0821 | 4000 | 0.0427 | 0.9906 | 0.9955 | 0.9879 | 0.9917 |
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- | 0.0385 | 0.1232 | 6000 | 0.0397 | 0.9900 | 0.9973 | 0.9851 | 0.9912 |
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- | 0.0315 | 0.1642 | 8000 | 0.0328 | 0.9913 | 0.9961 | 0.9885 | 0.9923 |
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- | 0.0326 | 0.2053 | 10000 | 0.0317 | 0.9914 | 0.9944 | 0.9905 | 0.9924 |
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- | 0.0331 | 0.2464 | 12000 | 0.0317 | 0.9917 | 0.9965 | 0.9888 | 0.9926 |
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- | 0.0383 | 0.2874 | 14000 | 0.0302 | 0.9919 | 0.9947 | 0.9909 | 0.9928 |
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- | 0.0299 | 0.3285 | 16000 | 0.0296 | 0.9919 | 0.9953 | 0.9904 | 0.9929 |
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- | 0.0299 | 0.3696 | 18000 | 0.0294 | 0.9921 | 0.9959 | 0.9901 | 0.9930 |
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- | 0.0322 | 0.4106 | 20000 | 0.0297 | 0.9921 | 0.9970 | 0.9890 | 0.9930 |
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- | 0.0323 | 0.4517 | 22000 | 0.0288 | 0.9922 | 0.9959 | 0.9903 | 0.9931 |
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- | 0.0309 | 0.4927 | 24000 | 0.0292 | 0.9921 | 0.9972 | 0.9889 | 0.9930 |
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- | 0.0325 | 0.5338 | 26000 | 0.0284 | 0.9921 | 0.9949 | 0.9912 | 0.9930 |
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- | 0.0292 | 0.5749 | 28000 | 0.0279 | 0.9923 | 0.9961 | 0.9903 | 0.9932 |
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- | 0.0265 | 0.6159 | 30000 | 0.0289 | 0.9923 | 0.9953 | 0.9911 | 0.9932 |
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- | 0.0291 | 0.6570 | 32000 | 0.0279 | 0.9924 | 0.9963 | 0.9903 | 0.9933 |
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- | 0.0307 | 0.6981 | 34000 | 0.0276 | 0.9926 | 0.9965 | 0.9904 | 0.9934 |
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- | 0.0286 | 0.7391 | 36000 | 0.0273 | 0.9926 | 0.9967 | 0.9903 | 0.9935 |
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- | 0.0287 | 0.7802 | 38000 | 0.0269 | 0.9927 | 0.9970 | 0.9901 | 0.9935 |
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- | 0.0284 | 0.8212 | 40000 | 0.0278 | 0.9925 | 0.9958 | 0.9910 | 0.9934 |
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- | 0.0258 | 0.8623 | 42000 | 0.0274 | 0.9927 | 0.9965 | 0.9905 | 0.9935 |
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- | 0.0245 | 0.9034 | 44000 | 0.0273 | 0.9927 | 0.9967 | 0.9904 | 0.9935 |
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- | 0.0324 | 0.9444 | 46000 | 0.0269 | 0.9927 | 0.9964 | 0.9907 | 0.9935 |
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- | 0.0311 | 0.9855 | 48000 | 0.0269 | 0.9927 | 0.9966 | 0.9906 | 0.9936 |
 
 
 
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  ### Framework versions
@@ -122,4 +91,4 @@ The following hyperparameters were used during training:
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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
 
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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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  metrics:
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  - accuracy
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  - precision
 
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  model-index:
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  - name: mdeberta-v3-base-name-classifier-v2
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  results: []
 
 
 
 
 
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  ---
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+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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+ should probably proofread and complete it, then remove this comment. -->
 
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+ # mdeberta-v3-base-name-classifier-v2
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+ This model is a fine-tuned version of [microsoft/mdeberta-v3-base](https://huggingface.co/microsoft/mdeberta-v3-base) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.0216
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+ - Accuracy: 0.9942
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+ - Precision: 0.9983
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+ - Recall: 0.9913
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+ - F1: 0.9948
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  ## Model description
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+ More information needed
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ ## Intended uses & limitations
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+ More information needed
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+ ## Training and evaluation data
 
 
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+ More information needed
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  ## Training procedure
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
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  |:-------------:|:------:|:-----:|:---------------:|:--------:|:---------:|:------:|:------:|
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+ | 0.047 | 0.0359 | 2000 | 0.0390 | 0.9903 | 0.9974 | 0.9851 | 0.9912 |
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+ | 0.0322 | 0.0718 | 4000 | 0.0346 | 0.9921 | 0.9968 | 0.9890 | 0.9929 |
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+ | 0.0325 | 0.1076 | 6000 | 0.0293 | 0.9924 | 0.9970 | 0.9893 | 0.9932 |
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+ | 0.0342 | 0.1435 | 8000 | 0.0264 | 0.9927 | 0.9973 | 0.9895 | 0.9934 |
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+ | 0.0301 | 0.1794 | 10000 | 0.0260 | 0.9929 | 0.9967 | 0.9905 | 0.9936 |
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+ | 0.0291 | 0.2153 | 12000 | 0.0259 | 0.9931 | 0.9984 | 0.9893 | 0.9938 |
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+ | 0.0246 | 0.2511 | 14000 | 0.0263 | 0.9931 | 0.9971 | 0.9905 | 0.9938 |
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+ | 0.0321 | 0.2870 | 16000 | 0.0264 | 0.9934 | 0.9988 | 0.9893 | 0.9940 |
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+ | 0.0256 | 0.3229 | 18000 | 0.0250 | 0.9935 | 0.9980 | 0.9903 | 0.9941 |
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+ | 0.0234 | 0.3588 | 20000 | 0.0260 | 0.9934 | 0.9969 | 0.9912 | 0.9940 |
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+ | 0.0246 | 0.3946 | 22000 | 0.0246 | 0.9935 | 0.9975 | 0.9909 | 0.9942 |
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+ | 0.0238 | 0.4305 | 24000 | 0.0252 | 0.9932 | 0.9961 | 0.9917 | 0.9938 |
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+ | 0.0263 | 0.4664 | 26000 | 0.0238 | 0.9936 | 0.9976 | 0.9910 | 0.9943 |
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+ | 0.0234 | 0.5023 | 28000 | 0.0250 | 0.9936 | 0.9972 | 0.9913 | 0.9943 |
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+ | 0.0241 | 0.5382 | 30000 | 0.0230 | 0.9939 | 0.9978 | 0.9912 | 0.9945 |
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+ | 0.0238 | 0.5740 | 32000 | 0.0228 | 0.9939 | 0.9984 | 0.9907 | 0.9945 |
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+ | 0.0243 | 0.6099 | 34000 | 0.0239 | 0.9939 | 0.9993 | 0.9897 | 0.9945 |
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+ | 0.023 | 0.6458 | 36000 | 0.0228 | 0.9939 | 0.9980 | 0.9911 | 0.9945 |
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+ | 0.0252 | 0.6817 | 38000 | 0.0230 | 0.9941 | 0.9987 | 0.9907 | 0.9947 |
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+ | 0.0251 | 0.7175 | 40000 | 0.0223 | 0.9940 | 0.9977 | 0.9915 | 0.9946 |
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+ | 0.0217 | 0.7534 | 42000 | 0.0226 | 0.9940 | 0.9976 | 0.9916 | 0.9946 |
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+ | 0.0269 | 0.7893 | 44000 | 0.0220 | 0.9941 | 0.9981 | 0.9914 | 0.9947 |
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+ | 0.0227 | 0.8252 | 46000 | 0.0224 | 0.9939 | 0.9972 | 0.9918 | 0.9945 |
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+ | 0.026 | 0.8610 | 48000 | 0.0216 | 0.9942 | 0.9986 | 0.9911 | 0.9948 |
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+ | 0.0213 | 0.8969 | 50000 | 0.0220 | 0.9942 | 0.9983 | 0.9913 | 0.9948 |
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+ | 0.0233 | 0.9328 | 52000 | 0.0217 | 0.9942 | 0.9982 | 0.9913 | 0.9948 |
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+ | 0.0239 | 0.9687 | 54000 | 0.0216 | 0.9942 | 0.9983 | 0.9913 | 0.9948 |
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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
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