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_64_drop_0.3_rank_32_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: 0.7735665694849369
          - name: Recall
            type: recall
            value: 0.8240165631469979
          - name: F1
            type: f1
            value: 0.7979949874686717
          - name: Accuracy
            type: accuracy
            value: 0.9840550320092251

UNER_subword_tk_en_lora_alpha_64_drop_0.3_rank_32_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: 0.0607
Precision: 0.7736
Recall: 0.8240
F1: 0.7980
Accuracy: 0.9841

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	0.0899	0.5755	0.7143	0.6374	0.9740
0.1782	2.0	784	0.0651	0.6961	0.7919	0.7409	0.9799
0.0539	3.0	1176	0.0664	0.7144	0.8209	0.7640	0.9815
0.0435	4.0	1568	0.0581	0.7170	0.8209	0.7654	0.9821
0.0435	5.0	1960	0.0584	0.7321	0.8261	0.7763	0.9820
0.0385	6.0	2352	0.0571	0.7409	0.8230	0.7798	0.9827
0.0342	7.0	2744	0.0580	0.7433	0.8333	0.7857	0.9829
0.0313	8.0	3136	0.0578	0.7744	0.8282	0.8004	0.9846
0.0295	9.0	3528	0.0566	0.7588	0.8271	0.7915	0.9835
0.0295	10.0	3920	0.0564	0.7756	0.8302	0.8020	0.9848
0.0272	11.0	4312	0.0557	0.7597	0.8344	0.7953	0.9835
0.0256	12.0	4704	0.0585	0.7787	0.8157	0.7968	0.9841
0.0248	13.0	5096	0.0574	0.7812	0.8240	0.8020	0.9845
0.0248	14.0	5488	0.0577	0.7604	0.8344	0.7957	0.9836
0.023	15.0	5880	0.0583	0.7812	0.8282	0.8040	0.9845
0.0222	16.0	6272	0.0595	0.7733	0.8333	0.8022	0.9841
0.0205	17.0	6664	0.0603	0.7755	0.8261	0.8	0.9839
0.0207	18.0	7056	0.0605	0.7744	0.8282	0.8004	0.9840
0.0207	19.0	7448	0.0611	0.7770	0.8333	0.8042	0.9842
0.0203	20.0	7840	0.0607	0.7736	0.8240	0.7980	0.9841

Framework versions

Transformers 4.41.1
Pytorch 2.3.0+cu121
Datasets 2.18.0
Tokenizers 0.19.1