---
language:
- de
---

library_name: transformers
tags:
- Text Classification
- Pytorch
- Discourse Classification
- Roberta
---
# Roberta for German Discourse Classification

This is a xlm Roberta model finetuned on a German Discourse dataset of 60 discourses having a total over 10k sentences.


## How to use the model

```python
import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer

def get_label(sentence):
    vectors = tokenizer(sentence, return_tensors='pt').to(device)
    outputs = bert_model(**vectors).logits
    probs = torch.nn.functional.softmax(outputs, dim = 1)[0]
    bert_dict = {}
    keys = ['Externalization', 'Elicitation', 'Conflict', 'Acceptence', 'Integration', 'None']
    for i in range(len(keys)):
        bert_dict[keys[i]] = round(probs[i].item(), 3)
    return bert_dict

MODEL_NAME = 'RashidNLP/Roberta-German-Discourse'
MODEL_DIR = 'model'
CHECKPOINT_DIR = 'checkpoints'
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
OUTPUTS = 6

bert_model = AutoModelForSequenceClassification.from_pretrained(MODEL_NAME, num_labels = OUTPUTS).to(device)
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)

get_label("Gehst du zum Oktoberfest?")

```