metadata
license: agpl-3.0
tags:
- generated_from_trainer
datasets:
- mim_gold_ner
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: XLMR-ENIS-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: mim_gold_ner
type: mim_gold_ner
args: mim-gold-ner
metrics:
- name: Precision
type: precision
value: 0.8685291700903862
- name: Recall
type: recall
value: 0.841273450824332
- name: F1
type: f1
value: 0.8546840706942359
- name: Accuracy
type: accuracy
value: 0.9824748714976435
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: 0.0940
- Precision: 0.8685
- Recall: 0.8413
- F1: 0.8547
- Accuracy: 0.9825
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 |
---|---|---|---|---|---|---|---|
0.0564 | 1.0 | 2904 | 0.0943 | 0.8505 | 0.8118 | 0.8307 | 0.9798 |
0.0321 | 2.0 | 5808 | 0.0907 | 0.8610 | 0.8235 | 0.8419 | 0.9814 |
0.0198 | 3.0 | 8712 | 0.0940 | 0.8685 | 0.8413 | 0.8547 | 0.9825 |
Framework versions
- Transformers 4.11.2
- Pytorch 1.9.0+cu102
- Datasets 1.12.1
- Tokenizers 0.10.3