Instructions to use bertin-project/bertin-base-random-exp-512seqlen with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bertin-project/bertin-base-random-exp-512seqlen with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="bertin-project/bertin-base-random-exp-512seqlen")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("bertin-project/bertin-base-random-exp-512seqlen") model = AutoModelForMaskedLM.from_pretrained("bertin-project/bertin-base-random-exp-512seqlen", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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This is a **RoBERTa-base** model trained from scratch in Spanish.
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The training dataset is [mc4](https://huggingface.co/datasets/bertin-project/mc4-es-sampled ) subsampling documents to a total of about 50 million examples. Sampling is random.
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This model
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Please see our main [card](https://huggingface.co/bertin-project/bertin-roberta-base-spanish) for more information.
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This is a **RoBERTa-base** model trained from scratch in Spanish.
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The training dataset is [mc4](https://huggingface.co/datasets/bertin-project/mc4-es-sampled ) subsampling documents to a total of about 50 million examples. Sampling is random.
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This model continued training from [sequence length 128](https://huggingface.co/bertin-project/bertin-base-random) using 20.000 steps for length 512.
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Please see our main [card](https://huggingface.co/bertin-project/bertin-roberta-base-spanish) for more information.
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