metadata

license: apache-2.0
base_model: bert-base-cased
tags:
  - generated_from_trainer
datasets:
  - conll2002
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: bert-finetuned-ner-cfv
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: conll2002
          type: conll2002
          config: es
          split: validation
          args: es
        metrics:
          - name: Precision
            type: precision
            value: 0.807683615819209
          - name: Recall
            type: recall
            value: 0.8212316176470589
          - name: F1
            type: f1
            value: 0.8144012760624361
          - name: Accuracy
            type: accuracy
            value: 0.974075543714453

bert-finetuned-ner-cfv

This model is a fine-tuned version of bert-base-cased on the conll2002 dataset. It achieves the following results on the evaluation set:

Loss: 0.1851
Precision: 0.8077
Recall: 0.8212
F1: 0.8144
Accuracy: 0.9741

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: 4e-05
train_batch_size: 24
eval_batch_size: 24
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 17

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	1.0	347	0.1278	0.7284	0.7475	0.7378	0.9646
0.1176	2.0	694	0.1212	0.7509	0.7806	0.7654	0.9681
0.0453	3.0	1041	0.1156	0.8062	0.8116	0.8089	0.9730
0.0453	4.0	1388	0.1270	0.8081	0.8031	0.8056	0.9720
0.0233	5.0	1735	0.1298	0.8145	0.8231	0.8187	0.9746
0.0145	6.0	2082	0.1431	0.7950	0.8091	0.8020	0.9728
0.0145	7.0	2429	0.1501	0.8103	0.8166	0.8135	0.9734
0.009	8.0	2776	0.1553	0.8118	0.8157	0.8138	0.9738
0.0061	9.0	3123	0.1572	0.7891	0.8084	0.7986	0.9720
0.0061	10.0	3470	0.1589	0.8142	0.8196	0.8169	0.9739
0.005	11.0	3817	0.1671	0.8092	0.8148	0.8120	0.9733
0.0032	12.0	4164	0.1716	0.8066	0.8139	0.8102	0.9733
0.0031	13.0	4511	0.1767	0.8025	0.8169	0.8096	0.9731
0.0031	14.0	4858	0.1756	0.8096	0.8217	0.8156	0.9741
0.0023	15.0	5205	0.1845	0.8109	0.8157	0.8133	0.9739
0.0018	16.0	5552	0.1850	0.8090	0.8203	0.8146	0.9739
0.0018	17.0	5899	0.1851	0.8077	0.8212	0.8144	0.9741

Framework versions

Transformers 4.41.2
Pytorch 2.3.0+cu121
Datasets 2.19.2
Tokenizers 0.19.1