|
--- |
|
license: mit |
|
tags: |
|
- generated_from_trainer |
|
datasets: |
|
- lg-ner |
|
metrics: |
|
- precision |
|
- recall |
|
- f1 |
|
- accuracy |
|
model-index: |
|
- name: luganda-ner-v1 |
|
results: |
|
- task: |
|
name: Token Classification |
|
type: token-classification |
|
dataset: |
|
name: lg-ner |
|
type: lg-ner |
|
config: lug |
|
split: test |
|
args: lug |
|
metrics: |
|
- name: Precision |
|
type: precision |
|
value: 0.7532580364900087 |
|
- name: Recall |
|
type: recall |
|
value: 0.7416595380667237 |
|
- name: F1 |
|
type: f1 |
|
value: 0.7474137931034481 |
|
- name: Accuracy |
|
type: accuracy |
|
value: 0.9492845117845118 |
|
--- |
|
|
|
<!-- 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. --> |
|
|
|
# luganda-ner-v1 |
|
|
|
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the lg-ner dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.2432 |
|
- Precision: 0.7533 |
|
- Recall: 0.7417 |
|
- F1: 0.7474 |
|
- Accuracy: 0.9493 |
|
|
|
## 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: 8 |
|
- eval_batch_size: 8 |
|
- 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 | 1.0 | 261 | 0.3950 | 0.5892 | 0.4380 | 0.5025 | 0.9104 | |
|
| 0.5722 | 2.0 | 522 | 0.2869 | 0.6306 | 0.6484 | 0.6394 | 0.9311 | |
|
| 0.5722 | 3.0 | 783 | 0.2300 | 0.7047 | 0.6758 | 0.6900 | 0.9452 | |
|
| 0.2424 | 4.0 | 1044 | 0.2293 | 0.6793 | 0.7340 | 0.7056 | 0.9426 | |
|
| 0.2424 | 5.0 | 1305 | 0.2208 | 0.7952 | 0.7074 | 0.7488 | 0.9497 | |
|
| 0.1564 | 6.0 | 1566 | 0.2345 | 0.7104 | 0.7408 | 0.7253 | 0.9447 | |
|
| 0.1564 | 7.0 | 1827 | 0.2312 | 0.6956 | 0.7605 | 0.7266 | 0.9456 | |
|
| 0.112 | 8.0 | 2088 | 0.2404 | 0.7673 | 0.7417 | 0.7542 | 0.9500 | |
|
| 0.112 | 9.0 | 2349 | 0.2303 | 0.7698 | 0.7553 | 0.7625 | 0.9531 | |
|
| 0.0879 | 10.0 | 2610 | 0.2432 | 0.7533 | 0.7417 | 0.7474 | 0.9493 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.26.1 |
|
- Pytorch 1.13.1+cu116 |
|
- Datasets 2.10.1 |
|
- Tokenizers 0.13.2 |
|
|
