Instructions to use research-backup/xlm-roberta-large-trimmed-pt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use research-backup/xlm-roberta-large-trimmed-pt with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="research-backup/xlm-roberta-large-trimmed-pt")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("research-backup/xlm-roberta-large-trimmed-pt") model = AutoModelForMaskedLM.from_pretrained("research-backup/xlm-roberta-large-trimmed-pt") - Notebooks
- Google Colab
- Kaggle
YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
Vocabulary Trimmed xlm-roberta-large: vocabtrimmer/xlm-roberta-large-trimmed-pt
This model is a trimmed version of xlm-roberta-large by vocabtrimmer, a tool for trimming vocabulary of language models to compress the model size.
Following table shows a summary of the trimming process.
| xlm-roberta-large | vocabtrimmer/xlm-roberta-large-trimmed-pt | |
|---|---|---|
| parameter_size_full | 560,142,482 | 372,108,282 |
| parameter_size_embedding | 256,002,048 | 68,151,296 |
| vocab_size | 250,002 | 66,554 |
| compression_rate_full | 100.0 | 66.43 |
| compression_rate_embedding | 100.0 | 26.62 |
Following table shows the parameter used to trim vocabulary.
| language | dataset | dataset_column | dataset_name | dataset_split | target_vocab_size | min_frequency |
|---|---|---|---|---|---|---|
| pt | vocabtrimmer/mc4_validation | text | pt | validation | 2 |
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