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-finetuned-conll_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: 0.8720365189221028
          - name: Recall
            type: recall
            value: 0.8429893238434164
          - name: F1
            type: f1
            value: 0.8572669368847712
          - name: Accuracy
            type: accuracy
            value: 0.9857922913838598

XLMR-ENIS-finetuned-ner-finetuned-conll_ner

This model is a fine-tuned version of vesteinn/XLMR-ENIS-finetuned-ner on the mim_gold_ner dataset. It achieves the following results on the evaluation set:

Loss: 0.0770
Precision: 0.8720
Recall: 0.8430
F1: 0.8573
Accuracy: 0.9858

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
0.0461	1.0	2904	0.0647	0.8588	0.8107	0.8341	0.9842
0.0244	2.0	5808	0.0704	0.8691	0.8296	0.8489	0.9849
0.0132	3.0	8712	0.0770	0.8720	0.8430	0.8573	0.9858

Framework versions

Transformers 4.11.3
Pytorch 1.9.0+cu111
Datasets 1.12.1
Tokenizers 0.10.3