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--- |
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license: cc-by-nc-4.0 |
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language: |
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- en |
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tags: |
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- English |
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- RoBERTa-base |
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- Text Classification |
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pipeline_tag: text-classification |
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--- |
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# RoBERTa base Fine-Tuned for Proposal Sentence Classification |
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## Overview |
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- **Language**: English |
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- **Model Name**: oeg/SciBERT-Repository-Proposal |
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## Description |
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This model is a fine-tuned allenai/scibert_scivocab_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. |
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## How to use |
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To use this model in Python: |
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```python |
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from transformers import AutoTokenizer, AutoModelForSequenceClassification |
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import torch |
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tokenizer = AutoTokenizer.from_pretrained("allenai/scibert_scivocab_uncased") |
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model = AutoModelForSequenceClassification.from_pretrained("scibert-model") |
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sentence = "Your input sentence here." |
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inputs = tokenizer(sentence, return_tensors="pt") |
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outputs = model(**inputs) |
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probabilities = torch.nn.functional.softmax(outputs.logits, dim=1) |
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