Text Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
text-embeddings-inference
Instructions to use Jeska/VaccinChatSentenceClassifierDutch_fromBERTjeDIAL with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jeska/VaccinChatSentenceClassifierDutch_fromBERTjeDIAL with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jeska/VaccinChatSentenceClassifierDutch_fromBERTjeDIAL")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jeska/VaccinChatSentenceClassifierDutch_fromBERTjeDIAL") model = AutoModelForSequenceClassification.from_pretrained("Jeska/VaccinChatSentenceClassifierDutch_fromBERTjeDIAL") - Notebooks
- Google Colab
- Kaggle
VaccinChatSentenceClassifierDutch_fromBERTjeDIAL
This model is a fine-tuned version of Jeska/BertjeWDialDataQA20k on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.8355
- Accuracy: 0.6322
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: 3.0
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 3.4418 | 1.0 | 1457 | 2.3866 | 0.5406 |
| 1.7742 | 2.0 | 2914 | 1.9365 | 0.6069 |
| 1.1313 | 3.0 | 4371 | 1.8355 | 0.6322 |
Framework versions
- Transformers 4.13.0.dev0
- Pytorch 1.10.0
- Datasets 1.16.1
- Tokenizers 0.10.3
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