Instructions to use vocabtrimmer/mt5-small-trimmed-it with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vocabtrimmer/mt5-small-trimmed-it with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("vocabtrimmer/mt5-small-trimmed-it") model = AutoModelForSeq2SeqLM.from_pretrained("vocabtrimmer/mt5-small-trimmed-it", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Check out the documentation for more information.
Vocabulary Trimmed google/mt5-small: vocabtrimmer/mt5-small-trimmed-it
This model is a trimmed version of google/mt5-small by vocabtrimmer, a tool for trimming vocabulary of language models to compress the model size.
Following table shows a summary of the trimming process.
| google/mt5-small | vocabtrimmer/mt5-small-trimmed-it | |
|---|---|---|
| parameter_size_full | 300,176,768 | 157,783,424 |
| parameter_size_embedding | 256,114,688 | 113,721,344 |
| vocab_size | 250,112 | 111,056 |
| compression_rate_full | 100.0 | 52.56 |
| compression_rate_embedding | 100.0 | 44.4 |
Following table shows the parameter used to trim vocabulary.
| language | dataset | dataset_column | dataset_name | dataset_split | target_vocab_size | min_frequency |
|---|---|---|---|---|---|---|
| it | vocabtrimmer/mc4_validation | text | it | validation | 2 |
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