Instructions to use sshleifer/bart-tiny-random with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sshleifer/bart-tiny-random with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("sshleifer/bart-tiny-random") model = AutoModelForSeq2SeqLM.from_pretrained("sshleifer/bart-tiny-random", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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Create README.md
Verified signature## Model description Meta AI’s BART is a powerful model used to transform incomplete text into coherent text, and bart-tiny-random is to BART what Lego bricks are to real bricks. It is a placeholder, a dummy model, to facilitate developers who need to test their programs which will eventually go on to use non-random, standard BART models. #### Architecture: BART #### Status: Randomly initialized ## Intended uses & limitations #### Uses i. Test and debug ML pipelines ii. Run unit tests and continuous integration checks iii. Verifying code works as intended without using the actual model, thereby saving system resources and user time #### Limitations i. Outputs are random jargon and should not be interpreted as valid ii. Not suitable for real-world natural language processing ### How to use Use the same way as other BART models, through the HuggingFace library. For instance: from transformers import AutoTokenizer,AutoModelForSeq2SeqLM tokenizer=AutoToken…2d34c99
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