Instructions to use vocabtrimmer/xlm-v-base-tweet-sentiment-es-trimmed-es-60000 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vocabtrimmer/xlm-v-base-tweet-sentiment-es-trimmed-es-60000 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="vocabtrimmer/xlm-v-base-tweet-sentiment-es-trimmed-es-60000")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("vocabtrimmer/xlm-v-base-tweet-sentiment-es-trimmed-es-60000") model = AutoModelForSequenceClassification.from_pretrained("vocabtrimmer/xlm-v-base-tweet-sentiment-es-trimmed-es-60000") - Notebooks
- Google Colab
- Kaggle
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Check out the documentation for more information.
Vocabulary Trimmed cardiffnlp/xlm-v-base-tweet-sentiment-es: vocabtrimmer/xlm-v-base-tweet-sentiment-es-trimmed-es-60000
This model is a trimmed version of cardiffnlp/xlm-v-base-tweet-sentiment-es by vocabtrimmer, a tool for trimming vocabulary of language models to compress the model size.
Following table shows a summary of the trimming process.
| cardiffnlp/xlm-v-base-tweet-sentiment-es | vocabtrimmer/xlm-v-base-tweet-sentiment-es-trimmed-es-60000 | |
|---|---|---|
| parameter_size_full | 778,495,491 | 132,125,955 |
| parameter_size_embedding | 692,451,072 | 46,081,536 |
| vocab_size | 901,629 | 60,002 |
| compression_rate_full | 100.0 | 16.97 |
| compression_rate_embedding | 100.0 | 6.65 |
Following table shows the parameter used to trim vocabulary.
| language | dataset | dataset_column | dataset_name | dataset_split | target_vocab_size | min_frequency |
|---|---|---|---|---|---|---|
| es | vocabtrimmer/mc4_validation | text | es | validation | 60000 | 2 |
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