metadata

license: mit
base_model: xlm-roberta-base
tags:
  - generated_from_trainer
datasets:
  - universalner/universal_ner
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: UNER_subword_tk_en_lora_alpha_256_drop_0.3_rank_128_seed_42
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: universalner/universal_ner en_ewt
          type: universalner/universal_ner
          config: en_ewt
          split: validation
          args: en_ewt
        metrics:
          - name: Precision
            type: precision
            value: 0.7770204479065238
          - name: Recall
            type: recall
            value: 0.8260869565217391
          - name: F1
            type: f1
            value: 0.8008028098344204
          - name: Accuracy
            type: accuracy
            value: 0.9841743210465624

UNER_subword_tk_en_lora_alpha_256_drop_0.3_rank_128_seed_42

This model is a fine-tuned version of xlm-roberta-base on the universalner/universal_ner en_ewt dataset. It achieves the following results on the evaluation set:

Loss: 0.0751
Precision: 0.7770
Recall: 0.8261
F1: 0.8008
Accuracy: 0.9842

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: 0.0001
train_batch_size: 32
eval_batch_size: 32
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 20.0

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	1.0	392	0.0746	0.6978	0.7578	0.7266	0.9775
0.1317	2.0	784	0.0618	0.7088	0.7888	0.7467	0.9807
0.0475	3.0	1176	0.0578	0.7483	0.8157	0.7806	0.9840
0.037	4.0	1568	0.0550	0.7439	0.8271	0.7833	0.9837
0.037	5.0	1960	0.0573	0.7468	0.8364	0.7891	0.9827
0.0305	6.0	2352	0.0581	0.7458	0.8230	0.7825	0.9833
0.0259	7.0	2744	0.0603	0.7683	0.8375	0.8014	0.9840
0.0237	8.0	3136	0.0622	0.7754	0.8219	0.7980	0.9843
0.0197	9.0	3528	0.0618	0.7759	0.8209	0.7978	0.9840
0.0197	10.0	3920	0.0664	0.7814	0.8178	0.7992	0.9845
0.0174	11.0	4312	0.0638	0.7751	0.8137	0.7939	0.9841
0.0152	12.0	4704	0.0678	0.7783	0.8251	0.8010	0.9845
0.0146	13.0	5096	0.0663	0.7871	0.8116	0.7992	0.9845
0.0146	14.0	5488	0.0678	0.7819	0.8313	0.8058	0.9849
0.0123	15.0	5880	0.0702	0.7862	0.8261	0.8057	0.9844
0.0115	16.0	6272	0.0727	0.7872	0.8271	0.8067	0.9846
0.0098	17.0	6664	0.0730	0.7952	0.8240	0.8094	0.9849
0.01	18.0	7056	0.0754	0.7891	0.8251	0.8067	0.9848
0.01	19.0	7448	0.0749	0.7706	0.8240	0.7964	0.9839
0.0091	20.0	7840	0.0751	0.7770	0.8261	0.8008	0.9842

Framework versions

Transformers 4.41.1
Pytorch 2.3.0+cu121
Datasets 2.18.0
Tokenizers 0.19.1