Instructions to use research-backup/mt5-base-koquad-qg-trimmed with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use research-backup/mt5-base-koquad-qg-trimmed with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("research-backup/mt5-base-koquad-qg-trimmed") model = AutoModelForSeq2SeqLM.from_pretrained("research-backup/mt5-base-koquad-qg-trimmed") - Notebooks
- Google Colab
- Kaggle
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Check out the documentation for more information.
Vocabulary Trimmed lmqg/mt5-base-koquad-qg: vocabtrimmer/mt5-base-koquad-qg-trimmed
This model is a trimmed version of lmqg/mt5-base-koquad-qg 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-base-koquad-qg | vocabtrimmer/mt5-base-koquad-qg-trimmed | |
|---|---|---|
| parameter_size_full | 582,384,384 | 310,905,600 |
| parameter_size_embedding | 384,155,136 | 112,676,352 |
| vocab_size | 250,101 | 73,357 |
| compression_rate_full | 100.0 | 53.38 |
| compression_rate_embedding | 100.0 | 29.33 |
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 | 2 |
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