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
| { | |
| "model_type": "model2vec", | |
| "architectures": [ | |
| "StaticModel" | |
| ], | |
| "tokenizer_name": "FacebookAI/xlm-roberta-large-finetuned-conll03-english", | |
| "apply_pca": 256, | |
| "apply_zipf": null, | |
| "sif_coefficient": 0.0001, | |
| "hidden_dim": 256, | |
| "seq_length": 1000000, | |
| "normalize": true | |
| } |