Instructions to use amcoff/classify_skolmat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use amcoff/classify_skolmat with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="amcoff/classify_skolmat")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("amcoff/classify_skolmat") model = AutoModelForSequenceClassification.from_pretrained("amcoff/classify_skolmat") - Notebooks
- Google Colab
- Kaggle
File size: 411 Bytes
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license: mit
datasets:
- amcoff/skolmat
language:
- sv
library_name: transformers
pipeline_tag: text-classification
widget:
- text: "Kycklingwok med äggnudlar och sojasås"
- text: "Kökets val"
---
```python
from transformers import pipeline
nlp = pipeline(
"text-classification",
model="amcoff/classify_skolmat",
tokenizer="KBLab/bert-base-swedish-cased",
)
nlp("Kökets val")
```
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