Instructions to use hiiamsid/hit5-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hiiamsid/hit5-base with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("hiiamsid/hit5-base") model = AutoModelForSeq2SeqLM.from_pretrained("hiiamsid/hit5-base") - Notebooks
- Google Colab
- Kaggle
This is a smaller version of the google/mt5-base model with only hindi embeddings left.
- The original model has 582M parameters, with 237M of them being input and output embeddings.
- After shrinking the
sentencepiecevocabulary from 250K to 25K (top 25K Hindi tokens) the number of model parameters reduced to 237M parameters, and model size reduced from 2.2GB to 0.9GB - 42% of the original one.
Citing & Authors
- Model : google/mt5-base
- Reference: cointegrated/rut5-base
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