Translation
Transformers
PyTorch
TensorFlow
JAX
Rust
t5
text-generation
summarization
text-generation-inference
Instructions to use baffo32/t5-base-ptmap with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use baffo32/t5-base-ptmap 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="baffo32/t5-base-ptmap")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelWithLMHead tokenizer = AutoTokenizer.from_pretrained("baffo32/t5-base-ptmap") model = AutoModelWithLMHead.from_pretrained("baffo32/t5-base-ptmap", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.ptmap from baffo32/t5-base-ptmap: direct link, hf CLI and curl.
- Browser
- Download file 892 MB
-
https://huggingface.co/baffo32/t5-base-ptmap/resolve/refs%2Fpr%2F1/pytorch_model.ptmap
- Command line
-
hf download hf://baffo32/t5-base-ptmap@refs/pr/1/pytorch_model.ptmap
-
curl -L -o pytorch_model.ptmap https://huggingface.co/baffo32/t5-base-ptmap/resolve/refs%2Fpr%2F1/pytorch_model.ptmap
892 MB
- Xet hash:
- 5be81acd8fd5d4b69ee669b72fd9994ddbfcd2983384b987acd8a703a32a544e
- Size of remote file:
- 892 MB
- SHA256:
- e51f7ffebbcc0025898dcddcfa19e0cd5324b9dd21fa7c55af11fe701b66fa26
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