Instructions to use vocabtrimmer/xlm-v-base-trimmed-en-30000 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vocabtrimmer/xlm-v-base-trimmed-en-30000 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="vocabtrimmer/xlm-v-base-trimmed-en-30000")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("vocabtrimmer/xlm-v-base-trimmed-en-30000") model = AutoModelForMaskedLM.from_pretrained("vocabtrimmer/xlm-v-base-trimmed-en-30000") - Notebooks
- Google Colab
- Kaggle
YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
Vocabulary Trimmed facebook/xlm-v-base: vocabtrimmer/xlm-v-base-trimmed-en-30000
This model is a trimmed version of facebook/xlm-v-base by vocabtrimmer, a tool for trimming vocabulary of language models to compress the model size.
Following table shows a summary of the trimming process.
| facebook/xlm-v-base | vocabtrimmer/xlm-v-base-trimmed-en-30000 | |
|---|---|---|
| parameter_size_full | 779,396,349 | 109,115,186 |
| parameter_size_embedding | 692,451,072 | 23,041,536 |
| vocab_size | 901,629 | 30,002 |
| compression_rate_full | 100.0 | 14.0 |
| compression_rate_embedding | 100.0 | 3.33 |
Following table shows the parameter used to trim vocabulary.
| language | dataset | dataset_column | dataset_name | dataset_split | target_vocab_size | min_frequency |
|---|---|---|---|---|---|---|
| en | vocabtrimmer/mc4_validation | text | en | validation | 30000 | 2 |
- Downloads last month
- 4