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:
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)
Inference Providers
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