|
--- |
|
license: apache-2.0 |
|
base_model: bert-base-multilingual-cased |
|
tags: |
|
- generated_from_trainer |
|
metrics: |
|
- precision |
|
- recall |
|
- f1 |
|
- accuracy |
|
model-index: |
|
- name: ner-bert |
|
results: [] |
|
--- |
|
|
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
|
should probably proofread and complete it, then remove this comment. --> |
|
|
|
# ner-bert |
|
|
|
This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on an unknown dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.0002 |
|
- Precision: 1.0 |
|
- Recall: 0.9993 |
|
- F1: 0.9997 |
|
- Accuracy: 1.0000 |
|
|
|
## 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: 4 |
|
- eval_batch_size: 4 |
|
- seed: 42 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: linear |
|
- num_epochs: 1 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
|
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
|
| 0.0005 | 0.1 | 250 | 0.0047 | 0.9998 | 0.9861 | 0.9929 | 0.9994 | |
|
| 0.009 | 0.2 | 500 | 0.0041 | 0.9961 | 0.9864 | 0.9912 | 0.9994 | |
|
| 0.0004 | 0.3 | 750 | 0.0024 | 0.9977 | 0.9895 | 0.9936 | 0.9995 | |
|
| 0.0001 | 0.4 | 1000 | 0.0010 | 0.9984 | 0.9975 | 0.9980 | 0.9999 | |
|
| 0.0001 | 0.51 | 1250 | 0.0008 | 1.0 | 0.9975 | 0.9987 | 0.9999 | |
|
| 0.0001 | 0.61 | 1500 | 0.0005 | 1.0 | 0.9975 | 0.9987 | 0.9999 | |
|
| 0.0003 | 0.71 | 1750 | 0.0003 | 1.0 | 0.9991 | 0.9995 | 1.0000 | |
|
| 0.0001 | 0.81 | 2000 | 0.0002 | 1.0 | 0.9993 | 0.9997 | 1.0000 | |
|
| 0.0 | 0.91 | 2250 | 0.0002 | 1.0 | 0.9993 | 0.9997 | 1.0000 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.37.2 |
|
- Pytorch 2.2.0+cu121 |
|
- Datasets 2.15.0 |
|
- Tokenizers 0.15.0 |
|
|
