RoBERTa base Fine-Tuned for Proposal Sentence Classification
Overview
- Language: English
- Model Name: oeg/SciBERT-Repository-Proposal
Description
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.
How to use
To use this model in Python:
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
tokenizer = AutoTokenizer.from_pretrained("allenai/scibert_scivocab_uncased")
model = AutoModelForSequenceClassification.from_pretrained("scibert-model")
sentence = "Your input sentence here."
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
probabilities = torch.nn.functional.softmax(outputs.logits, dim=1)
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