Instructions to use Commandante/german-sentiment-bert-retrained-20240110 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Commandante/german-sentiment-bert-retrained-20240110 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Commandante/german-sentiment-bert-retrained-20240110")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Commandante/german-sentiment-bert-retrained-20240110") model = AutoModelForSequenceClassification.from_pretrained("Commandante/german-sentiment-bert-retrained-20240110") - Notebooks
- Google Colab
- Kaggle
german-sentiment-bert-retrained-20240110
This model is a fine-tuned version of oliverguhr/german-sentiment-bert on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.3965
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: 8e-06
- train_batch_size: 20
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 120
- num_epochs: 7
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 2.2379 | 1.0 | 3 | 1.6068 |
| 2.2379 | 2.0 | 6 | 1.5979 |
| 2.2379 | 3.0 | 9 | 1.5805 |
| 2.2379 | 4.0 | 12 | 1.5571 |
| 2.2379 | 5.0 | 15 | 1.5176 |
| 2.2379 | 6.0 | 18 | 1.4626 |
| 2.2379 | 7.0 | 21 | 1.3965 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Tokenizers 0.15.0
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Model tree for Commandante/german-sentiment-bert-retrained-20240110
Base model
oliverguhr/german-sentiment-bert