metadata
license: agpl-3.0
tags:
- generated_from_trainer
datasets:
- mim_gold_ner
metrics:
- precision
- recall
- f1
- accuracy
base_model: vesteinn/XLMR-ENIS
model-index:
- name: XLMR-ENIS-finetuned-ner
results:
- task:
type: token-classification
name: Token Classification
dataset:
name: mim_gold_ner
type: mim_gold_ner
args: mim-gold-ner
metrics:
- type: precision
value: 0.8713799976550592
name: Precision
- type: recall
value: 0.8450255827174531
name: Recall
- type: f1
value: 0.8580004617871162
name: F1
- type: accuracy
value: 0.9827265378338392
name: Accuracy
XLMR-ENIS-finetuned-ner
This model is a fine-tuned version of vesteinn/XLMR-ENIS on the mim_gold_ner dataset. It achieves the following results on the evaluation set:
- Loss: 0.0941
- Precision: 0.8714
- Recall: 0.8450
- F1: 0.8580
- Accuracy: 0.9827
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 |
|---|---|---|---|---|---|---|---|
| 0.0572 | 1.0 | 2904 | 0.0998 | 0.8586 | 0.8171 | 0.8373 | 0.9802 |
| 0.0313 | 2.0 | 5808 | 0.0868 | 0.8666 | 0.8288 | 0.8473 | 0.9822 |
| 0.0199 | 3.0 | 8712 | 0.0941 | 0.8714 | 0.8450 | 0.8580 | 0.9827 |
Framework versions
- Transformers 4.11.2
- Pytorch 1.9.0+cu102
- Datasets 1.12.1
- Tokenizers 0.10.3