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
File size: 315 Bytes
ced70cf | 1 2 3 4 5 6 7 8 9 10 11 12 13 | {
"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
} |