Instructions to use Commandante/german-party-sentiment-bert-111-synonyms-5e-5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Commandante/german-party-sentiment-bert-111-synonyms-5e-5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Commandante/german-party-sentiment-bert-111-synonyms-5e-5")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Commandante/german-party-sentiment-bert-111-synonyms-5e-5") model = AutoModelForSequenceClassification.from_pretrained("Commandante/german-party-sentiment-bert-111-synonyms-5e-5", device_map="auto") - Notebooks
- Google Colab
- Kaggle
german-party-sentiment-bert-111-synonyms-5e-5
This model is a fine-tuned version of mdraw/german-news-sentiment-bert on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.0597
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: 5e-05
- 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 |
|---|---|---|---|
| 1.2321 | 1.0 | 25 | 1.0597 |
| 1.0831 | 2.0 | 50 | 1.1794 |
| 1.0831 | 3.0 | 75 | 1.1942 |
| 0.8683 | 4.0 | 100 | 1.4692 |
| 0.8683 | 5.0 | 125 | 1.6251 |
| 0.4833 | 6.0 | 150 | 1.8107 |
| 0.4833 | 7.0 | 175 | 2.1145 |
Framework versions
- Transformers 4.37.2
- Pytorch 2.2.0+cu121
- Tokenizers 0.15.1
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Model tree for Commandante/german-party-sentiment-bert-111-synonyms-5e-5
Base model
mdraw/german-news-sentiment-bert