metadata

license: mit
base_model: xlm-roberta-large
tags:
  - generated_from_trainer
datasets:
  - shipping_label_ner
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: ner_roberta_model
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: shipping_label_ner
          type: shipping_label_ner
          config: shipping_label_ner
          split: validation
          args: shipping_label_ner
        metrics:
          - name: Precision
            type: precision
            value: 0.5272727272727272
          - name: Recall
            type: recall
            value: 0.7837837837837838
          - name: F1
            type: f1
            value: 0.6304347826086956
          - name: Accuracy
            type: accuracy
            value: 0.7796610169491526

ner_roberta_model

This model is a fine-tuned version of xlm-roberta-large on the shipping_label_ner dataset. It achieves the following results on the evaluation set:

Loss: 2.0623
Precision: 0.5273
Recall: 0.7838
F1: 0.6304
Accuracy: 0.7797

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: 4
eval_batch_size: 2
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 50

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	1.0	14	1.1206	0.3125	0.4054	0.3529	0.6610
No log	2.0	28	0.7363	0.5128	0.5405	0.5263	0.7119
No log	3.0	42	0.6219	0.5333	0.6486	0.5854	0.7542
No log	4.0	56	0.7328	0.4727	0.7027	0.5652	0.7627
No log	5.0	70	0.8181	0.5	0.7297	0.5934	0.7542
No log	6.0	84	0.8485	0.5185	0.7568	0.6154	0.7627
No log	7.0	98	0.9692	0.5	0.7027	0.5843	0.7542
No log	8.0	112	0.9842	0.4915	0.7838	0.6042	0.7458
No log	9.0	126	1.1196	0.5	0.7838	0.6105	0.7542
No log	10.0	140	1.2147	0.5	0.7838	0.6105	0.7542
No log	11.0	154	1.4110	0.5	0.7568	0.6022	0.7712
No log	12.0	168	1.2104	0.5370	0.7838	0.6374	0.7881
No log	13.0	182	1.4145	0.5283	0.7568	0.6222	0.7797
No log	14.0	196	1.4939	0.5179	0.7838	0.6237	0.7712
No log	15.0	210	1.5558	0.5273	0.7838	0.6304	0.7797
No log	16.0	224	1.5639	0.5273	0.7838	0.6304	0.7797
No log	17.0	238	1.5208	0.5179	0.7838	0.6237	0.7712
No log	18.0	252	1.4787	0.5918	0.7838	0.6744	0.7966
No log	19.0	266	1.3946	0.5283	0.7568	0.6222	0.7797
No log	20.0	280	1.6672	0.5370	0.7838	0.6374	0.7881
No log	21.0	294	1.5746	0.5185	0.7568	0.6154	0.7712
No log	22.0	308	1.8881	0.5091	0.7568	0.6087	0.7712
No log	23.0	322	1.5084	0.5370	0.7838	0.6374	0.7881
No log	24.0	336	1.7922	0.5091	0.7568	0.6087	0.7712
No log	25.0	350	1.7265	0.5273	0.7838	0.6304	0.7797
No log	26.0	364	1.7467	0.5273	0.7838	0.6304	0.7797
No log	27.0	378	2.0162	0.5	0.7568	0.6022	0.7627
No log	28.0	392	1.9460	0.5	0.7568	0.6022	0.7627
No log	29.0	406	1.8957	0.5091	0.7568	0.6087	0.7712
No log	30.0	420	1.9941	0.5	0.7568	0.6022	0.7627
No log	31.0	434	1.9095	0.5	0.7568	0.6022	0.7712
No log	32.0	448	1.8920	0.5273	0.7838	0.6304	0.7797
No log	33.0	462	1.9310	0.5091	0.7568	0.6087	0.7712
No log	34.0	476	1.9830	0.5091	0.7568	0.6087	0.7712
No log	35.0	490	2.0445	0.5091	0.7568	0.6087	0.7712
0.2599	36.0	504	2.1138	0.5091	0.7568	0.6087	0.7712
0.2599	37.0	518	2.0024	0.5091	0.7568	0.6087	0.7797
0.2599	38.0	532	2.0004	0.5091	0.7568	0.6087	0.7712
0.2599	39.0	546	2.0725	0.5091	0.7568	0.6087	0.7712
0.2599	40.0	560	2.0507	0.5091	0.7568	0.6087	0.7712
0.2599	41.0	574	2.0548	0.5091	0.7568	0.6087	0.7712
0.2599	42.0	588	2.1176	0.5091	0.7568	0.6087	0.7712
0.2599	43.0	602	2.0946	0.5091	0.7568	0.6087	0.7712
0.2599	44.0	616	2.1211	0.5	0.7568	0.6022	0.7627
0.2599	45.0	630	2.1103	0.5091	0.7568	0.6087	0.7712
0.2599	46.0	644	2.0876	0.5	0.7568	0.6022	0.7627
0.2599	47.0	658	2.0910	0.5179	0.7838	0.6237	0.7712
0.2599	48.0	672	2.0800	0.5179	0.7838	0.6237	0.7712
0.2599	49.0	686	2.0584	0.5273	0.7838	0.6304	0.7797
0.2599	50.0	700	2.0623	0.5273	0.7838	0.6304	0.7797

Framework versions

Transformers 4.39.1
Pytorch 2.2.1+cu121
Datasets 2.18.0
Tokenizers 0.15.2