Instructions to use LKarlo/vinucmer-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LKarlo/vinucmer-small with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="LKarlo/vinucmer-small")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("LKarlo/vinucmer-small") model = AutoModelForMaskedLM.from_pretrained("LKarlo/vinucmer-small", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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training roberta structure with 4786611 samples, 24054 test samples, 500 vocab size, 3 hidden layers, 256 hidden size, 4 attention heads, 0.15 mlm probability, 10 num process, 512 max length, 0.005 train test split, 50 min sub seq length, 2000 max sub seq length, 42 seed
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training roberta structure with 4786611 samples, 24054 test samples, 500 vocab size, 3 hidden layers, 256 hidden size, 4 attention heads, 0.15 mlm probability, 10 num process, 512 max length, 0.005 train test split, 50 min sub seq length, 2000 max sub seq length, 42 seed
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training roberta structure with 4808259 samples, 2406 test samples, 500 vocab size, 3 hidden layers, 256 hidden size, 4 attention heads, 0.15 mlm probability, 10 num process, 512 max length, 0.0005 train test split, 50 min sub seq length, 2000 max sub seq length, 42 seed
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training roberta structure with 4808259 samples, 2406 test samples, 500 vocab size, 3 hidden layers, 256 hidden size, 4 attention heads, 0.15 mlm probability, 10 num process, 512 max length, 0.0005 train test split, 50 min sub seq length, 2000 max sub seq length, 42 seed
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initial commit
192ee1c