|
--- |
|
license: mit |
|
tags: |
|
- generated_from_trainer |
|
datasets: |
|
- wikiann |
|
metrics: |
|
- precision |
|
- recall |
|
- f1 |
|
- accuracy |
|
model-index: |
|
- name: xlmr-finetuned-ner |
|
results: |
|
- task: |
|
name: Token Classification |
|
type: token-classification |
|
dataset: |
|
name: wikiann |
|
type: wikiann |
|
args: et |
|
metrics: |
|
- name: Precision |
|
type: precision |
|
value: 0.9044097027481772 |
|
- name: Recall |
|
type: recall |
|
value: 0.9136978539556626 |
|
- name: F1 |
|
type: f1 |
|
value: 0.9090300532008596 |
|
- name: Accuracy |
|
type: accuracy |
|
value: 0.9649304793632428 |
|
--- |
|
|
|
<!-- 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. --> |
|
|
|
# xlmr-finetuned-ner |
|
|
|
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the wikiann dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.1395 |
|
- Precision: 0.9044 |
|
- Recall: 0.9137 |
|
- F1: 0.9090 |
|
- Accuracy: 0.9649 |
|
|
|
## 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: 3 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
|
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
|
| 0.4215 | 1.0 | 938 | 0.1650 | 0.8822 | 0.8781 | 0.8802 | 0.9529 | |
|
| 0.1559 | 2.0 | 1876 | 0.1412 | 0.9018 | 0.9071 | 0.9045 | 0.9631 | |
|
| 0.1051 | 3.0 | 2814 | 0.1395 | 0.9044 | 0.9137 | 0.9090 | 0.9649 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.19.4 |
|
- Pytorch 1.11.0+cu113 |
|
- Datasets 2.2.2 |
|
- Tokenizers 0.12.1 |
|
|
