Instructions to use arraypowerplay/mt5-small-amazon-reviews-es-en with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use arraypowerplay/mt5-small-amazon-reviews-es-en with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "summarization" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # pip install "transformers<5.0.0" from transformers import pipeline pipe = pipeline("summarization", model="arraypowerplay/mt5-small-amazon-reviews-es-en")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("arraypowerplay/mt5-small-amazon-reviews-es-en") model = AutoModelForSeq2SeqLM.from_pretrained("arraypowerplay/mt5-small-amazon-reviews-es-en", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from arraypowerplay/mt5-small-amazon-reviews-es-en: direct link, hf CLI and curl.
- Browser
- Download file 5.33 kB
-
https://huggingface.co/arraypowerplay/mt5-small-amazon-reviews-es-en/resolve/main/training_args.bin
- Command line
-
hf download hf://arraypowerplay/mt5-small-amazon-reviews-es-en/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/arraypowerplay/mt5-small-amazon-reviews-es-en/resolve/main/training_args.bin
5.33 kB
- Xet hash:
- f52240fbbe6b948ec719002e2543316bf569b8871bf90ca594887c8841d049b2
- Size of remote file:
- 5.33 kB
- SHA256:
- c342cc957bf06613df7f4dbfee56642f128fed4a85ab988abcaae480f6c232ff
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