Zefty/distilbert-ner-email-org
By Zefty · ↓ 0 · ♥ 0 · Updated 2025-08-11
What's in this repo
This page lists the files in Zefty/distilbert-ner-email-org, including model.onnx, model_bnb4.onnx, model_fp16.onnx and more.
- 8 ONNX models (.onnx files)
- 18 files, including 8 .onnx, 5 .json, 1 .md, 1 .safetensors, 1 .bin, 1 .txt
- Last updated 2025-08-11
ONNX files
| File | Size | Download |
|---|---|---|
| onnx/model.onnx | — | Download |
| onnx/model_bnb4.onnx | — | Download |
| onnx/model_fp16.onnx | — | Download |
| onnx/model_int8.onnx | — | Download |
| onnx/model_q4.onnx | — | Download |
| onnx/model_q4f16.onnx | — | Download |
| onnx/model_quantized.onnx | — | Download |
| onnx/model_uint8.onnx | — | Download |
How to use
These ONNX files can be loaded with ONNX Runtime. To inspect a model graph, upload the file to the Netron viewer. Download links point to the original files on Hugging Face.
README
Zefty/distilbert-ner-email-org
distilbert-ner-email-org is a fine-tuned version of dslim/distilbert-NER on a set of job application emails. The model is fine-tuned specifically to identify the organizations (ORG) entity, thus it CANNOT identify location (LOC), person (PER), and Miscellaneous (MISC), which is available in the original model. This model is fine-tuned specifically to identify the organizations for a personal side-project of mine to extract out companies from job application emails.
How to use
This model can be utilized with the Transformers pipeline for NER.
from transformers import AutoTokenizer, AutoModelForTokenClassification
from transformers import pipeline
tokenizer = AutoTokenizer.from_pretrained("Zefty/distilbert-ner-email-org")
model = AutoModelForTokenClassification.from_pretrained("Zefty/distilbert-ner-email-org")
nlp = pipeline("ner", model=model, tokenizer=tokenizer, aggregation_strategy="first")
example = "Thank you for Applying to Amazon!"
ner_results = nlp(example)
print(ner_results)
Training data
This model was fine-tuned on a set of job application emails. Instead of using the full tokens from the CoNLL-2003 English Dataset, this dataset only includes the ORG token.
| Abbreviation | Description |
|---|---|
| O | Outside of a named entity |
| B-ORG | Beginning of an organization right after another organization |
| I-ORG | organization |
Eval results
| Metric | Score |
|---|---|
| Loss | 0.0898725837469101 |
| Precision | 0.7111111111111111 |
| Recall | 0.8205128205128205 |
| F1 | 0.7619047619047619 |
| Accuracy | 0.9760986309658876 |
Model info
Created: 2025-08-11
Updated: 2025-08-11
View on Hugging FaceFiles (18)
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