Instructions to use Phazel/fa_core_news_trf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- spaCy
How to use Phazel/fa_core_news_trf with spaCy:
!pip install https://huggingface.co/Phazel/fa_core_news_trf/resolve/main/fa_core_news_trf-any-py3-none-any.whl # Using spacy.load(). import spacy nlp = spacy.load("fa_core_news_trf") # Importing as module. import fa_core_news_trf nlp = fa_core_news_trf.load() - Notebooks
- Google Colab
- Kaggle
Upload README.md with huggingface_hub
Browse files
README.md
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@@ -54,8 +54,12 @@ split (23,825 tokens), timing the pipe only. Warmup pass discarded.
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| Device | Batch | Words/s |
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| cpu (Intel(R) Core(TM) i5-7200U CPU @ 2.50GHz) | 32 | 187 |
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## Sources
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| Source | Author | Licence |
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| Device | Batch | Words/s |
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| gpu:0 (Tesla T4, 15360 MiB) | 32 | 8,320 |
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| cpu (Intel(R) Xeon(R) CPU @ 2.00GHz) | 32 | 336 |
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| cpu (Intel(R) Core(TM) i5-7200U CPU @ 2.50GHz) | 32 | 187 |
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A transformer pipeline is GPU-bound: the T4 is 25x the CPU on the same machine. On CPU this runs roughly 25x slower than the `sm`/`md`/`lg` tiers, which is the price of the accuracy below.
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## Sources
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| Source | Author | Licence |
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