Instructions to use Jarbas/m2v-256-xlm-roberta-large-finetuned-conll03-english with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Model2Vec
How to use Jarbas/m2v-256-xlm-roberta-large-finetuned-conll03-english with Model2Vec:
from model2vec import StaticModel model = StaticModel.from_pretrained("Jarbas/m2v-256-xlm-roberta-large-finetuned-conll03-english") - sentence-transformers
How to use Jarbas/m2v-256-xlm-roberta-large-finetuned-conll03-english with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Jarbas/m2v-256-xlm-roberta-large-finetuned-conll03-english") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f4c590636250981f7c8054621abcf3c4cb19d103c63381ecb9b0e8304e314165
- Size of remote file:
- 128 MB
- SHA256:
- 99f1d8cf1c0686a760c5c0296b8c9a990c9664c8334bcee0337c424bc0887b03
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.