Sentence Similarity
sentence-transformers
PyTorch
Transformers
xlm-roberta
feature-extraction
MT Evaluation
Metrics
Evaluation
text-embeddings-inference
Instructions to use AnanyaCoder/XLsim_en-de with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use AnanyaCoder/XLsim_en-de with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("AnanyaCoder/XLsim_en-de") 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 AnanyaCoder/XLsim_en-de with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("AnanyaCoder/XLsim_en-de") model = AutoModel.from_pretrained("AnanyaCoder/XLsim_en-de", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download similarity_evaluation_sts-test_results.csv from AnanyaCoder/XLsim_en-de: direct link, hf CLI and curl.
- Browser
- Download file 299 Bytes
-
https://huggingface.co/AnanyaCoder/XLsim_en-de/resolve/main/similarity_evaluation_sts-test_results.csv
- Command line
-
hf download hf://AnanyaCoder/XLsim_en-de/similarity_evaluation_sts-test_results.csv
-
curl -L -o similarity_evaluation_sts-test_results.csv https://huggingface.co/AnanyaCoder/XLsim_en-de/resolve/main/similarity_evaluation_sts-test_results.csv
299 Bytes
| epoch,steps,cosine_pearson,cosine_spearman,euclidean_pearson,euclidean_spearman,manhattan_pearson,manhattan_spearman,dot_pearson,dot_spearman | |
| -1,-1,0.5254911431784646,0.5255135904971285,0.5104816018951371,0.5008859862096693,0.511620300299343,0.5030306127344961,0.3248038335029235,0.3292063904563908 | |