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)