metadata

license: apache-2.0
base_model: dslim/distilbert-NER
tags:
  - generated_from_trainer
datasets:
  - transformer_dataset_ner
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: huner_ncbi_disease_dslim
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: transformer_dataset_ner
          type: transformer_dataset_ner
          config: ncbi_disease
          split: validation
          args: ncbi_disease
        metrics:
          - name: Precision
            type: precision
            value: 0.8325183374083129
          - name: Recall
            type: recall
            value: 0.8653113087674714
          - name: F1
            type: f1
            value: 0.8485981308411215
          - name: Accuracy
            type: accuracy
            value: 0.9849891909996041

huner_ncbi_disease_dslim

This model is a fine-tuned version of dslim/distilbert-NER on the transformer_dataset_ner dataset. It achieves the following results on the evaluation set:

Loss: 0.1484
Precision: 0.8325
Recall: 0.8653
F1: 0.8486
Accuracy: 0.9850

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: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 20

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.1243	1.0	667	0.0669	0.7013	0.8412	0.7649	0.9787
0.0512	2.0	1334	0.0656	0.7825	0.8412	0.8108	0.9818
0.0221	3.0	2001	0.0744	0.7908	0.8501	0.8194	0.9822
0.0107	4.0	2668	0.1022	0.7940	0.8475	0.8199	0.9808
0.008	5.0	3335	0.1055	0.7818	0.8602	0.8191	0.9816
0.0057	6.0	4002	0.1173	0.8067	0.8590	0.832	0.9830
0.0027	7.0	4669	0.1188	0.8188	0.8501	0.8342	0.9834
0.0022	8.0	5336	0.1229	0.8080	0.8450	0.8261	0.9826
0.0019	9.0	6003	0.1341	0.8007	0.8526	0.8258	0.9834
0.0019	10.0	6670	0.1360	0.8045	0.8628	0.8326	0.9822
0.0011	11.0	7337	0.1376	0.8163	0.8640	0.8395	0.9838
0.0008	12.0	8004	0.1447	0.8007	0.8577	0.8282	0.9833
0.0006	13.0	8671	0.1381	0.8139	0.8615	0.8370	0.9839
0.0005	14.0	9338	0.1398	0.8297	0.8666	0.8477	0.9843
0.0004	15.0	10005	0.1404	0.8232	0.8640	0.8431	0.9842
0.0003	16.0	10672	0.1486	0.8329	0.8551	0.8439	0.9838
0.0	17.0	11339	0.1469	0.8114	0.8691	0.8393	0.9837
0.0002	18.0	12006	0.1500	0.8297	0.8602	0.8447	0.9843
0.0001	19.0	12673	0.1489	0.8315	0.8653	0.8481	0.9849
0.0	20.0	13340	0.1484	0.8325	0.8653	0.8486	0.9850

Framework versions

Transformers 4.35.2
Pytorch 2.1.0+cu121
Datasets 2.16.1
Tokenizers 0.15.1