Instructions to use hedtorresca/Multilingual-MiniLM-L12-H384-fine-tunning2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hedtorresca/Multilingual-MiniLM-L12-H384-fine-tunning2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="hedtorresca/Multilingual-MiniLM-L12-H384-fine-tunning2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hedtorresca/Multilingual-MiniLM-L12-H384-fine-tunning2") model = AutoModelForSequenceClassification.from_pretrained("hedtorresca/Multilingual-MiniLM-L12-H384-fine-tunning2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 46639633c2d8de5dd92188e621a5ddee81cee902f482a892419e9fe17c4e6f4d
- Size of remote file:
- 471 MB
- SHA256:
- d59128bf1e22454ac1d79a966618dfd2d438706189484bd828de0d5f3baae1b6
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