Instructions to use unicamp-dl/mt5-base-mmarco-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use unicamp-dl/mt5-base-mmarco-v2 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("unicamp-dl/mt5-base-mmarco-v2") model = AutoModelForSeq2SeqLM.from_pretrained("unicamp-dl/mt5-base-mmarco-v2") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9b763c3c6d11a06bdb1369b22f41fd6214916bfca5fa020dd32afbd7789e0d7c
- Size of remote file:
- 2.33 GB
- SHA256:
- 41e2ae2eae6f9be089eb56416ee3419a41e4f5a1e82515e15e78d67e1ef5be36
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.