| --- |
| language: en |
| tags: |
| - text-classification |
| - pytorch |
| - roberta |
| - self-beliefs |
| - multi-class-classification |
| - multi-label-classification |
| license: mit |
| widget: |
| - text: I am the coolest person I know. |
| --- |
| |
| #### Overview |
|
|
| Model trained from [roberta-large](https://huggingface.co/roberta-large) on a dataset of human and LLM annotated self-beliefs for multi-label classification. |
|
|
| ### Training Details |
|
|
| Model training , hyper-parameters, and evaluation can be found in "Capturing Self-Beliefs in Natural Language" by Mangalik et al. 2024 |
|
|
| ### Inference |
|
|
| A sample way to use this model for classification |
|
|
| ```python |
| from transformers import pipeline |
| huggingface_model = 'sidmangalik/selfBERTa' |
| model = RobertaForSequenceClassification.from_pretrained(huggingface_model) |
| tokenizer = RobertaTokenizerFast.from_pretrained(huggingface_model, max_length = 512, padding="max_length", truncation=True) |
| |
| texts = ["I am the coolest person I know."] |
| |
| inputs = tokenizer(texts, max_length=512, padding="max_length", truncation=True, return_tensors='pt') |
| outputs = model(**inputs) |
| logits = outputs.logits |
| soft_logits = torch.softmax(logits, dim=1).tolist() |
| predicted_classes = np.argmax(soft_logits, axis=1) |
| ``` |
