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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?")
``` |