Instructions to use wilsonmarciliojr/mt5-small-finetuned-en-fr-news with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use wilsonmarciliojr/mt5-small-finetuned-en-fr-news 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="wilsonmarciliojr/mt5-small-finetuned-en-fr-news")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("wilsonmarciliojr/mt5-small-finetuned-en-fr-news") model = AutoModelForSeq2SeqLM.from_pretrained("wilsonmarciliojr/mt5-small-finetuned-en-fr-news", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8cffc4fca0013c4107e4abfb70a9ed9f1c4e4e210a90a2d58144462296dd885e
- Size of remote file:
- 5.5 kB
- SHA256:
- 7293b63510ab3dfe7eda0a867bde89827ba962443cc20cc6e2a2d2ead27f2ff5
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.