Instructions to use VladimML/mt5-small-News with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use VladimML/mt5-small-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="VladimML/mt5-small-News")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("VladimML/mt5-small-News") model = AutoModelForSeq2SeqLM.from_pretrained("VladimML/mt5-small-News", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from VladimML/mt5-small-News: direct link, hf CLI and curl.
- Browser
- Download file 16.3 MB
-
https://huggingface.co/VladimML/mt5-small-News/resolve/main/tokenizer.json
- Command line
-
hf download hf://VladimML/mt5-small-News/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/VladimML/mt5-small-News/resolve/main/tokenizer.json
16.3 MB
- Xet hash:
- 289a733fc8c08b6616cdf7f98d940a4683140445c10d41b465aeb2d80c450cc7
- Size of remote file:
- 16.3 MB
- SHA256:
- d7d2f49f56f45a052aba9d07f68db316a332e8627e30ae9341aa31b16b626508
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.