Sentence Similarity
sentence-transformers
Safetensors
Transformers
xlm-roberta
feature-extraction
text-embeddings-inference
Instructions to use dadashzadeh/xlm-roberta-base-test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use dadashzadeh/xlm-roberta-base-test with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("dadashzadeh/xlm-roberta-base-test") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Transformers
How to use dadashzadeh/xlm-roberta-base-test with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("dadashzadeh/xlm-roberta-base-test") model = AutoModel.from_pretrained("dadashzadeh/xlm-roberta-base-test", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| epoch,steps,cosine_pearson,cosine_spearman,euclidean_pearson,euclidean_spearman,manhattan_pearson,manhattan_spearman,dot_pearson,dot_spearman | |
| 0,-1,0.8355244163453055,0.6282459709409298,0.8339586108310645,0.6216201305288228,0.8316541367052884,0.6224529587431137,0.7847741806720513,0.6230549552739728 | |
| 1,-1,0.8476684996903473,0.6438837617957465,0.8459151905293754,0.6273070485318208,0.8443433034653669,0.6272330293261338,0.8179680560698438,0.6457642709192574 | |