metadata

license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - wikiann
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: albert-large-v2_ner_wikiann
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: wikiann
          type: wikiann
          args: en
        metrics:
          - name: Precision
            type: precision
            value: 0.8239671720684378
          - name: Recall
            type: recall
            value: 0.8374805598755832
          - name: F1
            type: f1
            value: 0.8306689103912495
          - name: Accuracy
            type: accuracy
            value: 0.926951922121784

albert-large-v2_ner_wikiann

This model is a fine-tuned version of albert-large-v2 on the wikiann dataset. It achieves the following results on the evaluation set:

Loss: 0.3416
Precision: 0.8240
Recall: 0.8375
F1: 0.8307
Accuracy: 0.9270

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: 1e-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: cosine
num_epochs: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.3451	1.0	2500	0.3555	0.7745	0.7850	0.7797	0.9067
0.2995	2.0	5000	0.2927	0.7932	0.8240	0.8083	0.9205
0.252	3.0	7500	0.2936	0.8094	0.8236	0.8164	0.9239
0.1676	4.0	10000	0.3302	0.8256	0.8359	0.8307	0.9268
0.1489	5.0	12500	0.3416	0.8240	0.8375	0.8307	0.9270

Framework versions

Transformers 4.20.1
Pytorch 1.11.0
Datasets 2.1.0
Tokenizers 0.12.1