german-sentiment-bert
This model was trained for sentiment classification of German language texts. To achieve the best results all model inputs needs to be preprocessed with the same procedure, that was applied during the training. To simplify the usage of the model, we provide a Python package that bundles the code need for the preprocessing and inferencing.
Params
109 M
Context
512
Downloads 30d
431 K
Likes
73
Download history
daily snapshots · 25 days450 K270 K
Sep 30Oct 8Oct 16Oct 24
Can you run it?
Estimated VRAM at 8K context unless noted. Pick your hardware to see the verdict per quantization.
| File | Quant | Size | Est. VRAM | Verdict on RTX 4090 · 24 GB |
|---|---|---|---|---|
| model.safetensors | f32 | 0.4 GB | 1.0 GB | ✅ Runs comfortably |
Estimate: file size × 1.1 + KV cache at 8K + 0.5 GB overhead. Not a benchmark — how we calculate this.
Specifications
- Architecture
- BertForSequenceClassification
- Parameters
- 109 M
- Tensor type
- F32
- Context length
- 512
- Vocabulary
- 30,000
- Layers / heads
- 12 / 12
- Licence
- mit
- First seen on the Hub
- 2022-03-02
- Training datasets
- undisclosed
- Added to our catalog
- 2026-09-30
Compare with any text-classification model