Text Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
text-embeddings-inference
Instructions to use evangeliazve/predict_department_v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use evangeliazve/predict_department_v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="evangeliazve/predict_department_v2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("evangeliazve/predict_department_v2") model = AutoModelForSequenceClassification.from_pretrained("evangeliazve/predict_department_v2") - Notebooks
- Google Colab
- Kaggle
predict_department_v2
This model is a fine-tuned version of bert-base-multilingual-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.3698
- F1: 0.9045
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: 4
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|---|---|---|---|---|
| 0.3916 | 1.0 | 11724 | 0.3966 | 0.8752 |
| 0.3037 | 2.0 | 23448 | 0.3324 | 0.8948 |
| 0.2323 | 3.0 | 35172 | 0.3351 | 0.9023 |
| 0.1675 | 4.0 | 46896 | 0.3698 | 0.9045 |
Framework versions
- Transformers 4.25.1
- Pytorch 1.13.1+cu117
- Datasets 2.8.0
- Tokenizers 0.13.2
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