Instructions to use Hailay/TigRoberta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Adapters
How to use Hailay/TigRoberta with Adapters:
from adapters import AutoAdapterModel model = AutoAdapterModel.from_pretrained("<base-model-id>") model.load_adapter("Hailay/TigRoberta", set_active=True) - Notebooks
- Google Colab
- Kaggle
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Verified signatureHere is how to use this model to get the features of a given text in PyTorch: from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained('Hailay/TigRoberta') model = AutoModelForMaskedLM.from_pretrained("Hailay/TigRoberta") # prepare input text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') # forward pass output = model(**encoded_input)d275456 -
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Verified signatureXLM-RoBERTa is a multilingual version of RoBERTa. It is pre-trained on 2.5TB of filtered CommonCrawl data containing 100 languages. We train on Tigriynaf9d38ad
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