metadata
license: mit
base_model: xlm-roberta-large
tags:
- generated_from_trainer
datasets:
- wikiann
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: xlm-roberta-large-ner-silvanus
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: wikiann
type: wikiann
config: id
split: validation
args: id
metrics:
- name: Precision
type: precision
value: 0.957203615098352
- name: Recall
type: recall
value: 0.9714054491502563
- name: F1
type: f1
value: 0.964252242602758
- name: Accuracy
type: accuracy
value: 0.9885975250441956
xlm-roberta-large-ner-silvanus
This model is a fine-tuned version of xlm-roberta-large on the wikiann dataset. It achieves the following results on the evaluation set:
- Loss: 0.0523
- Precision: 0.9572
- Recall: 0.9714
- F1: 0.9643
- Accuracy: 0.9886
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: 6
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 24
- 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 |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 285 | 0.0795 | 0.9222 | 0.9342 | 0.9282 | 0.9763 |
| 0.112 | 2.0 | 570 | 0.0613 | 0.9295 | 0.9560 | 0.9426 | 0.9844 |
| 0.112 | 3.0 | 855 | 0.0523 | 0.9572 | 0.9714 | 0.9643 | 0.9886 |
Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1