metadata
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
xlmr-finetuned-ner
This model is a fine-tuned version of 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