pabRomero/BERT-full-finetuned-ner-pablo
By pabRomero · ↓ 8 · ♥ 0 · Updated 2024-10-02
What's in this repo
This page lists the files in pabRomero/BERT-full-finetuned-ner-pablo, including model_quantized.onnx.
- 1 ONNX models (.onnx files)
- 30 files, including 9 .0, 4 .json, 3 .1, 3 .2, 3 .3, 2 .4
- Last updated 2024-10-02
ONNX files
| File | Size | Download |
|---|---|---|
| onnx/model_quantized.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
BERT-full-finetuned-ner-pablo
This model is a fine-tuned version of google-bert/bert-base-uncased on the n2c2 2018 dataset for the paper https://arxiv.org/abs/2409.19467. It achieves the following results on the evaluation set:
- Loss: 0.0854
- Precision: 0.7857
- Recall: 0.7899
- F1: 0.7878
- Accuracy: 0.9747
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: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 4
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 231 | 0.1015 | 0.7485 | 0.7440 | 0.7462 | 0.9703 |
| No log | 2.0 | 462 | 0.0878 | 0.7618 | 0.7750 | 0.7684 | 0.9728 |
| 0.2646 | 3.0 | 693 | 0.0859 | 0.7759 | 0.7912 | 0.7835 | 0.9737 |
| 0.2646 | 4.0 | 924 | 0.0854 | 0.7857 | 0.7899 | 0.7878 | 0.9747 |
Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1
Model info
Created: 2024-08-15
Updated: 2024-10-02
View on Hugging Face