metadata

license: apache-2.0
base_model: distilbert-base-cased
tags:
  - generated_from_trainer
datasets:
  - shipping_label_ner
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: ner_bert_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: 0.8192771084337349
          - name: Recall
            type: recall
            value: 0.9066666666666666
          - name: F1
            type: f1
            value: 0.8607594936708859
          - name: Accuracy
            type: accuracy
            value: 0.903954802259887

ner_bert_model

This model is a fine-tuned version of distilbert-base-cased on the shipping_label_ner dataset. It achieves the following results on the evaluation set:

Loss: 0.4675
Precision: 0.8193
Recall: 0.9067
F1: 0.8608
Accuracy: 0.9040

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: 8
eval_batch_size: 2
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 30

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	1.0	7	1.9567	0.0	0.0	0.0	0.4294
No log	2.0	14	1.7382	1.0	0.0133	0.0263	0.4350
No log	3.0	21	1.5156	0.56	0.1867	0.28	0.5424
No log	4.0	28	1.3070	0.5185	0.3733	0.4341	0.6215
No log	5.0	35	1.1073	0.6792	0.48	0.5625	0.6667
No log	6.0	42	0.9590	0.6970	0.6133	0.6525	0.7288
No log	7.0	49	0.8036	0.7324	0.6933	0.7123	0.7853
No log	8.0	56	0.7173	0.6860	0.7867	0.7329	0.8305
No log	9.0	63	0.5963	0.7778	0.84	0.8077	0.8814
No log	10.0	70	0.5354	0.7901	0.8533	0.8205	0.8870
No log	11.0	77	0.5048	0.8	0.8533	0.8258	0.8814
No log	12.0	84	0.4992	0.8293	0.9067	0.8662	0.9096
No log	13.0	91	0.4745	0.8205	0.8533	0.8366	0.8927
No log	14.0	98	0.4489	0.8608	0.9067	0.8831	0.9153
No log	15.0	105	0.4236	0.8608	0.9067	0.8831	0.9153
No log	16.0	112	0.4621	0.8193	0.9067	0.8608	0.9096
No log	17.0	119	0.4417	0.85	0.9067	0.8774	0.9209
No log	18.0	126	0.4642	0.8095	0.9067	0.8553	0.9040
No log	19.0	133	0.4244	0.85	0.9067	0.8774	0.9096
No log	20.0	140	0.4731	0.8193	0.9067	0.8608	0.9096
No log	21.0	147	0.4697	0.8193	0.9067	0.8608	0.9040
No log	22.0	154	0.4330	0.8293	0.9067	0.8662	0.9096
No log	23.0	161	0.4531	0.8193	0.9067	0.8608	0.9040
No log	24.0	168	0.4433	0.8193	0.9067	0.8608	0.9040
No log	25.0	175	0.4477	0.8095	0.9067	0.8553	0.9040
No log	26.0	182	0.4446	0.8293	0.9067	0.8662	0.9096
No log	27.0	189	0.4578	0.8293	0.9067	0.8662	0.9096
No log	28.0	196	0.4640	0.8293	0.9067	0.8662	0.9096
No log	29.0	203	0.4683	0.8193	0.9067	0.8608	0.9040
No log	30.0	210	0.4675	0.8193	0.9067	0.8608	0.9040

Framework versions

Transformers 4.39.1
Pytorch 2.2.1+cu121
Datasets 2.18.0
Tokenizers 0.15.2