Text Classification
Transformers
PyTorch
Safetensors
bert
Generated from Trainer
text-embeddings-inference
Instructions to use jonas/bert-base-uncased-finetuned-sdg-Mar23 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jonas/bert-base-uncased-finetuned-sdg-Mar23 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="jonas/bert-base-uncased-finetuned-sdg-Mar23")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("jonas/bert-base-uncased-finetuned-sdg-Mar23") model = AutoModelForSequenceClassification.from_pretrained("jonas/bert-base-uncased-finetuned-sdg-Mar23", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Updated README with Labelling
#1
by tsb97 - opened
When loading model using Transformers, The labelling for all the scores is like the following:
'SDG1' : scores[0],
'SDG10' : scores[1],
'SDG11' : scores[2],
'SDG12' : scores[3],
'SDG13' : scores[4],
'SDG14' : scores[5],
'SDG15' : scores[6],
'SDG16' : scores[7],
'SDG2' : scores[8],
'SDG3' : scores[9],
'SDG4' : scores[10],
'SDG5' : scores[11],
'SDG6' : scores[12],
'SDG7' : scores[13],
'SDG8' : scores[14],
'SDG9' : scores[15]
jonas changed pull request status to merged