Instructions to use sdadas/mt5-base-translator-en-pl with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sdadas/mt5-base-translator-en-pl with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "translation" 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("translation", model="sdadas/mt5-base-translator-en-pl")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("sdadas/mt5-base-translator-en-pl") model = AutoModelForSeq2SeqLM.from_pretrained("sdadas/mt5-base-translator-en-pl", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from sdadas/mt5-base-translator-en-pl: direct link, hf CLI and curl.
- Browser
- Download file 16.3 MB
-
https://huggingface.co/sdadas/mt5-base-translator-en-pl/resolve/main/tokenizer.json
- Command line
-
hf download hf://sdadas/mt5-base-translator-en-pl/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/sdadas/mt5-base-translator-en-pl/resolve/main/tokenizer.json
16.3 MB
- Xet hash:
- 30c92ef191178979616c243d60e7f3a6484ffb0968dfeec459064ff8846bc268
- Size of remote file:
- 16.3 MB
- SHA256:
- 99cc999819aaabf74898a252863b10d86fbcd86e8b3f65c118ff334ff85c5ea5
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