Text Classification
Transformers
Safetensors
English
roberta
English
RoBERTa-base
Text Classification
Instructions to use oeg/RoBERTa-Repository-Proposal with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use oeg/RoBERTa-Repository-Proposal with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="oeg/RoBERTa-Repository-Proposal")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("oeg/RoBERTa-Repository-Proposal") model = AutoModelForSequenceClassification.from_pretrained("oeg/RoBERTa-Repository-Proposal") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -29,7 +29,7 @@ from transformers import RobertaForSequenceClassification, RobertaTokenizer
|
|
| 29 |
import torch
|
| 30 |
|
| 31 |
tokenizer = RobertaTokenizer.from_pretrained("roberta-base")
|
| 32 |
-
model = RobertaForSequenceClassification.from_pretrained("
|
| 33 |
|
| 34 |
sentence = "Your input sentence here."
|
| 35 |
inputs = tokenizer(sentence, return_tensors="pt")
|
|
|
|
| 29 |
import torch
|
| 30 |
|
| 31 |
tokenizer = RobertaTokenizer.from_pretrained("roberta-base")
|
| 32 |
+
model = RobertaForSequenceClassification.from_pretrained("oeg/RoBERTa-Repository-Proposal")
|
| 33 |
|
| 34 |
sentence = "Your input sentence here."
|
| 35 |
inputs = tokenizer(sentence, return_tensors="pt")
|