Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use ViktorDo/EcoBERT-POWO_Lifecycle_Pretrained with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ViktorDo/EcoBERT-POWO_Lifecycle_Pretrained with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ViktorDo/EcoBERT-POWO_Lifecycle_Pretrained")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ViktorDo/EcoBERT-POWO_Lifecycle_Pretrained") model = AutoModelForSequenceClassification.from_pretrained("ViktorDo/EcoBERT-POWO_Lifecycle_Pretrained") - Notebooks
- Google Colab
- Kaggle
EcoBERT-POWO_Lifecycle_Pretrained
This model is a fine-tuned version of ViktorDo/EcoBERT-Pretrained on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0782
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: 3
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.0895 | 1.0 | 1704 | 0.0798 |
| 0.0795 | 2.0 | 3408 | 0.0769 |
| 0.065 | 3.0 | 5112 | 0.0782 |
Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
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