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distilbert-multilingual-cased_fine_tuned_
nicotaroni/distilbert-multilingual-cased_fine_tuned_
distilbert-multilingual-cased_fine_tuned_ is a text classification model from nicotaroni. Use it when you need a label for a piece of text. It is set up for transformers.
The model has been fine-tuned for a multilingual classification text task: it recognizes whether a real-estate advertisement is an agency advertisement (label = 1) or a private advertisement (label = 0).
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At a glance
- Task
- Text Classification
- Library
- transformers
- Model type
- distilbert
- Access
- Public
- Created
- Apr 20, 2023
- Updated
- Apr 21, 2023
- SHA
- 6bf0690a
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Model card
From the Hugging Face model README
Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->The model has been fine-tuned for a multilingual classification text task: it recognizes whether a real-estate advertisement is an agency advertisement (label = 1) or a private advertisement (label = 0).
- Developed by: [nicotaroni]
- Model type: [text-classification]
- Language(s) (NLP): [Multilingual]
- Finetuned from model [optional]: [distilbert-multilingual-cased]
How to Get Started with the Model
Use the code below to get started with the model:
from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
tokenizer = AutoTokenizer.from_pretrained("nicotaroni/distilbert-multilingual-cased_fine_tuned_", max_length=512,truncation=True)
model = AutoModelForSequenceClassification.from_pretrained("nicotaroni/distilbert-multilingual-cased_fine_tuned_")
classifier = pipeline("text-classification", model=model, tokenizer=tokenizer, truncation = True)
text = " real estate advertisement "
outputs = classifier(text)
print(outputs)
- Task
- Text Classification
- Library
- transformers
- Type
- distilbert
- Created
- Apr 20, 2023
- Updated
- Apr 21, 2023