Instructions to use lmqg/mt5-small-koquad-ae-trimmed-50000 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lmqg/mt5-small-koquad-ae-trimmed-50000 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("lmqg/mt5-small-koquad-ae-trimmed-50000") model = AutoModelForSeq2SeqLM.from_pretrained("lmqg/mt5-small-koquad-ae-trimmed-50000") - Notebooks
- Google Colab
- Kaggle
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Check out the documentation for more information.
Vocabulary Trimmed lmqg/mt5-small-koquad-ae: lmqg/mt5-small-koquad-ae-trimmed-50000
This model is a trimmed version of lmqg/mt5-small-koquad-ae by vocabtrimmer, a tool for trimming vocabulary of language models to compress the model size.
Following table shows a summary of the trimming process.
| lmqg/mt5-small-koquad-ae | lmqg/mt5-small-koquad-ae-trimmed-50000 | |
|---|---|---|
| parameter_size_full | 300,165,504 | 95,264,128 |
| parameter_size_embedding | 256,103,424 | 51,202,048 |
| vocab_size | 250,101 | 50,002 |
| compression_rate_full | 100.0 | 31.74 |
| compression_rate_embedding | 100.0 | 19.99 |
Following table shows the parameter used to trim vocabulary.
| language | dataset | dataset_column | dataset_name | dataset_split | target_vocab_size | min_frequency |
|---|---|---|---|---|---|---|
| ko | vocabtrimmer/mc4_validation | text | ko | validation | 50000 | 2 |
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