Summarization
Transformers
PyTorch
TensorFlow
TensorBoard
mt5
text2text-generation
Generated from Trainer
Instructions to use huggingface-course/mt5-small-finetuned-amazon-en-es with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use huggingface-course/mt5-small-finetuned-amazon-en-es 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="huggingface-course/mt5-small-finetuned-amazon-en-es")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("huggingface-course/mt5-small-finetuned-amazon-en-es") model = AutoModelForSeq2SeqLM.from_pretrained("huggingface-course/mt5-small-finetuned-amazon-en-es") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 86af2799e1a52b56bb0eb92c4ef7ff34b89876a3cd9de5a13ba1962e55d2d332
- Size of remote file:
- 1.2 GB
- SHA256:
- 20bbf017cf9d7d7ea90cbdc347d3dab8e12046dcde53938140d3e8d274f10bb4
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.