| --- |
| license: cc-by-nc-4.0 |
| language: |
| - en |
| tags: |
| - English |
| - Bert-base |
| - Text Classification |
| pipeline_tag: text-classification |
| --- |
| |
| # RoBERTa base Fine-Tuned for Proposal Sentence Classification |
|
|
| ## Overview |
|
|
| - **Language**: English |
| - **Model Name**: oeg/BERT-Repository-Proposal |
| |
| ## Description |
|
|
| This model is a fine-tuned bert base uncased model trained to classify sentences into two classes: proposal and non-proposal sentences. The training data includes sentences proposing a software or data repository. The model is trained to recognize and classify these sentences accurately. |
|
|
| ## How to use |
|
|
| To use this model in Python: |
|
|
| ```python |
| from transformers import RobertaForSequenceClassification, RobertaTokenizer |
| import torch |
| |
| tokenizer = RobertaTokenizer.from_pretrained("bert-repo-proposal-tokenizer") |
| model = RobertaForSequenceClassification.from_pretrained("bert-repo-proposal-model") |
| |
| sentence = "Your input sentence here." |
| inputs = tokenizer(sentence, return_tensors="pt") |
| outputs = model(**inputs) |
| probabilities = torch.nn.functional.softmax(outputs.logits, dim=1) |
| |
