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

- Loss: 0.4592
- Accuracy: 0.8486

  
## Usage

```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': [0.53142249584198, 0.26030370593070984, 0.09126164764165878, 0.06451434642076492, 0.052497804164886475]}


#example 2
sequence = "Wie lautet die Garantiezeit für dieses Produkt?"
candidate_labels = [
    "Garantiebedingungen", 
    "Kundendienst", 
    "Produktdetails", 
    "Reklamation", 
    "Kaufberatung"
]
pipe(sequence,candidate_labels)
>>
{'sequence': 'Wie lautet die Garantiezeit für dieses Produkt?',
'labels': ['Garantiebedingungen', 'Kundendienst', 'Produktdetails', 'Reklamation', 'Kaufberatung'],
'scores': [0.414899080991745, 0.2377401739358902, 0.1381743848323822, 0.12171833217144012, 0.08746808022260666]}


```

## 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

### Training results

| Training Loss | Epoch  | Step  | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:------:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.6429        | 0.1630 | 1000  | 0.5203          | 0.8004   | 0.8006 | 0.8009    | 0.8004 |
| 0.5715        | 0.3259 | 2000  | 0.5209          | 0.7964   | 0.7968 | 0.8005    | 0.7964 |
| 0.5897        | 0.4889 | 3000  | 0.5435          | 0.7924   | 0.7940 | 0.8039    | 0.7924 |
| 0.5701        | 0.6519 | 4000  | 0.5242          | 0.7880   | 0.7884 | 0.8078    | 0.7880 |
| 0.5238        | 0.8149 | 5000  | 0.4816          | 0.8233   | 0.8226 | 0.8263    | 0.8233 |
| 0.5285        | 0.9778 | 6000  | 0.4483          | 0.8265   | 0.8273 | 0.8303    | 0.8265 |
| 0.4302        | 1.1408 | 7000  | 0.4751          | 0.8209   | 0.8214 | 0.8277    | 0.8209 |
| 0.4163        | 1.3038 | 8000  | 0.4560          | 0.8285   | 0.8289 | 0.8344    | 0.8285 |
| 0.3942        | 1.4668 | 9000  | 0.4330          | 0.8414   | 0.8422 | 0.8454    | 0.8414 |
| 0.3875        | 1.6297 | 10000 | 0.4171          | 0.8430   | 0.8432 | 0.8455    | 0.8430 |
| 0.3639        | 1.7927 | 11000 | 0.4194          | 0.8442   | 0.8447 | 0.8487    | 0.8442 |
| 0.3768        | 1.9557 | 12000 | 0.4215          | 0.8474   | 0.8477 | 0.8492    | 0.8474 |
| 0.2443        | 2.1186 | 13000 | 0.4750          | 0.8390   | 0.8398 | 0.8452    | 0.8390 |
| 0.2404        | 2.2816 | 14000 | 0.4592          | 0.8486   | 0.8487 | 0.8505    | 0.8486 |
| 0.2154        | 2.4446 | 15000 | 0.4914          | 0.8418   | 0.8424 | 0.8466    | 0.8418 |
| 0.2157        | 2.6076 | 16000 | 0.4804          | 0.8454   | 0.8458 | 0.8488    | 0.8454 |
| 0.2249        | 2.7705 | 17000 | 0.4809          | 0.8466   | 0.8471 | 0.8507    | 0.8466 |
| 0.2204        | 2.9335 | 18000 | 0.4777          | 0.8466   | 0.8470 | 0.8502    | 0.8466 |


### Framework versions

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