onnx-community/greek_legal_bert_v2-finetuned-ner-V3-ONNX
By onnx-community · ↓ 13 · ♥ 0 · Updated 2026-03-21
What's in this repo
This page lists the files in onnx-community/greek_legal_bert_v2-finetuned-ner-V3-ONNX, including model.onnx, model_bnb4.onnx, model_fp16.onnx and more.
- 8 ONNX models (.onnx files)
- 16 files, including 8 .onnx, 5 .json, 1 .md, 1 .txt
- Last updated 2026-03-21
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
greek_legal_bert_v2-finetuned-ner-V3 (ONNX)
This is an ONNX version of amichailidis/greek_legal_bert_v2-finetuned-ner-V3. It was automatically converted and uploaded using this Hugging Face Space.
Usage with Transformers.js
See the pipeline documentation for token-classification: https://huggingface.co/docs/transformers.js/api/pipelines#module_pipelines.TokenClassificationPipeline
greek_legal_bert_v2-finetuned-ner-V4
This model is a fine-tuned version of amichailidis/greek_legal_bert_v2-finetuned-ner on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0659
- Precision: 0.9033
- Recall: 0.9373
- F1: 0.9200
- Accuracy: 0.9845
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.19 | 25 | 0.1246 | 0.8264 | 0.8190 | 0.8227 | 0.9680 |
| No log | 2.38 | 50 | 0.0729 | 0.8716 | 0.9247 | 0.8974 | 0.9809 |
| No log | 3.57 | 75 | 0.0553 | 0.8978 | 0.9444 | 0.9205 | 0.9851 |
| No log | 4.76 | 100 | 0.0591 | 0.8990 | 0.9409 | 0.9194 | 0.9852 |
| No log | 5.95 | 125 | 0.0598 | 0.9017 | 0.9373 | 0.9192 | 0.9849 |
| No log | 7.14 | 150 | 0.0628 | 0.9064 | 0.9373 | 0.9216 | 0.9842 |
| No log | 8.33 | 175 | 0.0636 | 0.9031 | 0.9355 | 0.9190 | 0.9843 |
| No log | 9.52 | 200 | 0.0659 | 0.9033 | 0.9373 | 0.9200 | 0.9845 |
Framework versions
- Transformers 4.23.0
- Pytorch 1.12.1+cu113
- Datasets 2.5.2
- Tokenizers 0.13.1
Model info
Created: 2026-03-21
Updated: 2026-03-21
View on Hugging Face