How to use from the
Use from the
Transformers library
# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-classification", model="Jeska/VaccinChatSentenceClassifierDutch_fromBERTje")
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("Jeska/VaccinChatSentenceClassifierDutch_fromBERTje")
model = AutoModelForSequenceClassification.from_pretrained("Jeska/VaccinChatSentenceClassifierDutch_fromBERTje")
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VaccinChatSentenceClassifierDutch_fromBERTje

This model is a fine-tuned version of GroNLP/bert-base-dutch-cased on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 0.6223
  • Accuracy: 0.9068

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: 15.0

Training results

Training Loss Epoch Step Validation Loss Accuracy
3.4666 1.0 1320 2.3355 0.5768
1.5293 2.0 2640 1.1118 0.8144
0.8031 3.0 3960 0.6362 0.8803
0.2985 4.0 5280 0.5119 0.8958
0.1284 5.0 6600 0.5023 0.8931
0.0842 6.0 7920 0.5246 0.9022
0.0414 7.0 9240 0.5581 0.9013
0.0372 8.0 10560 0.5721 0.9004
0.0292 9.0 11880 0.5469 0.9141
0.0257 10.0 13200 0.5871 0.9059
0.0189 11.0 14520 0.6181 0.9049
0.0104 12.0 15840 0.6184 0.9068
0.009 13.0 17160 0.6013 0.9049
0.0051 14.0 18480 0.6205 0.9059
0.0035 15.0 19800 0.6223 0.9068

Framework versions

  • Transformers 4.13.0.dev0
  • Pytorch 1.10.0
  • Datasets 1.16.1
  • Tokenizers 0.10.3
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