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license: mit |
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# Roberta for German text Classification |
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This is a xlm Roberta model finetuned on a German Discourse dataset of 60 discourses having a total over 10k sentences. |
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# Understanding the labels |
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**Externalization:** Emphasize situational factors that we dont have control over as the cause of behavior. For example "I had a really tough day at work and then when I got home, my cat got sick. It's just been one thing after another and it's really getting to me.". |
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**Elicitation:** Emphasize the role of the listener by asking questions or providing prompts. For example "Can you tell me more about what it feels like when you're anxious?". |
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**Conflict:** Attribute each other's behavior to dispositional factors (such as being short-sighted or inflexible). For example "You're not thinking about the big picture here!". |
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**Acceptance:** Accept the perspectives or experiences of others. For example "It sounds like you had a really hard day". |
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**Integration:** Combining multiple perspectives to create a more comprehensive understanding of the behavior of others. For example "What if we combined elements of both proposals to create something that incorporates the best of both worlds?". |
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## How to use the model |
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```python |
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import torch |
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from transformers import AutoModelForSequenceClassification, AutoTokenizer |
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def get_label(sentence): |
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vectors = tokenizer(sentence, return_tensors='pt').to(device) |
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outputs = bert_model(**vectors).logits |
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probs = torch.nn.functional.softmax(outputs, dim = 1)[0] |
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bert_dict = {} |
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keys = ['Externalization', 'Elicitation', 'Conflict', 'Acceptence', 'Integration', 'None'] |
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for i in range(len(keys)): |
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bert_dict[keys[i]] = round(probs[i].item(), 3) |
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return bert_dict |
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MODEL_NAME = 'RashidNLP/German-Text-Classification' |
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MODEL_DIR = 'model' |
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CHECKPOINT_DIR = 'checkpoints' |
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device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') |
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OUTPUTS = 6 |
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bert_model = AutoModelForSequenceClassification.from_pretrained(MODEL_NAME, num_labels = OUTPUTS).to(device) |
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tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME) |
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get_label("Gehst du zum Oktoberfest?") |
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``` |
