metadata

license: apache-2.0
base_model: distilbert-base-uncased
tags:
  - generated_from_trainer
datasets:
  - szeged_ner
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: test-train-model
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: szeged_ner
          type: szeged_ner
          config: business
          split: validation
          args: business
        metrics:
          - name: Precision
            type: precision
            value: 0.9325044404973357
          - name: Recall
            type: recall
            value: 0.9308510638297872
          - name: F1
            type: f1
            value: 0.9316770186335402
          - name: Accuracy
            type: accuracy
            value: 0.9925327242378986

test-train-model

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

Loss: 0.0319
Precision: 0.9325
Recall: 0.9309
F1: 0.9317
Accuracy: 0.9925

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: 16
eval_batch_size: 16
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.2029	1.0	511	0.0493	0.8734	0.8564	0.8648	0.9873
0.0756	2.0	1022	0.0381	0.8930	0.9025	0.8977	0.9897
0.0489	3.0	1533	0.0327	0.925	0.9184	0.9217	0.9921
0.0339	4.0	2044	0.0323	0.9385	0.9202	0.9293	0.9926
0.0258	5.0	2555	0.0319	0.9325	0.9309	0.9317	0.9925

Framework versions

Transformers 4.32.0
Pytorch 2.0.1+cu118
Datasets 2.14.4
Tokenizers 0.13.3