---
library_name: transformers
license: mit
base_model: deepset/gbert-large
tags:
- generated_from_trainer
model-index:
- name: german-zeroshot
  results: []
datasets:
- facebook/xnli
language:
- de
metrics:
- accuracy
pipeline_tag: zero-shot-classification
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# german-zeroshot

This model is a fine-tuned version of [deepset/gbert-large](https://huggingface.co/deepset/gbert-large) on facebook/xnli de dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.5051
- eval_accuracy: 0.8096
- eval_f1: 0.8102
- eval_precision: 0.8131
- eval_recall: 0.8096
- eval_runtime: 5.9824
- eval_samples_per_second: 416.224
- eval_steps_per_second: 13.038
- epoch: 0.4889
- step: 3000

```python
# Use a pipeline as a high-level helper

pipe = pipeline(
    "zero-shot-classification",
    model="kaixkhazaki/german-zeroshot",
    tokenizer="kaixkhazaki/german-zeroshot",
    device=0 if torch.cuda.is_available() else -1  # Use GPU if available
)

#Enter your text and possible candidates of classification
sequence = "Können Sie mir die Schritte zur Konfiguration eines VPN auf einem Linux-Server erklären?"
candidate_labels = [
    "Technische Dokumentation", 
    "IT-Support", 
    "Netzwerkadministration", 
    "Linux-Konfiguration", 
    "VPN-Setup"
]
pipe(sequence,candidate_labels)
>>
{'sequence': 'Können Sie mir die Schritte zur Konfiguration eines VPN auf einem Linux-Server erklären?',
'labels': ['VPN-Setup', 'Linux-Konfiguration', 'Netzwerkadministration', 'IT-Support', 'Technische Dokumentation'],
'scores': [0.3245040476322174, 0.32373329997062683, 0.16423103213310242, 0.09850211441516876, 0.08902951329946518]}

#example 2
sequence = "Können Sie mir die Schritte zur Konfiguration eines VPN auf einem Linux-Server erklären?"
candidate_labels = [
    "Technische Dokumentation", 
    "IT-Support", 
    "Netzwerkadministration", 
    "Linux-Konfiguration", 
    "VPN-Setup"
]
pipe(sequence,candidate_labels)
>>
{'sequence': 'Wie lautet die Garantiezeit für dieses Produkt?',
'labels': ['Garantiebedingungen', 'Produktdetails', 'Reklamation', 'Kundendienst', 'Kaufberatung'],
'scores': [0.4313304126262665, 0.2905466556549072, 0.10058070719242096, 0.09384352713823318, 0.08369863778352737]}

```

## 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: 64
- eval_batch_size: 32
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 500
- num_epochs: 3

### Framework versions

- Transformers 4.48.0.dev0
- Pytorch 2.4.1+cu121
- Datasets 3.1.0
- Tokenizers 0.21.0