Sentence Similarity
sentence-transformers
PyTorch
roberta
feature-extraction
text-embeddings-inference
Instructions to use jfarray/Model_all-distilroberta-v1_30_Epochs with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use jfarray/Model_all-distilroberta-v1_30_Epochs with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("jfarray/Model_all-distilroberta-v1_30_Epochs") 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] - Notebooks
- Google Colab
- Kaggle
Download similarity_evaluation_sts-test_results.csv from jfarray/Model_all-distilroberta-v1_30_Epochs: direct link, hf CLI and curl.
- Browser
- Download file 302 Bytes
-
https://huggingface.co/jfarray/Model_all-distilroberta-v1_30_Epochs/resolve/e063a3c85bae1d45e4eb9f8f7768d2169199638e/similarity_evaluation_sts-test_results.csv
- Command line
-
hf download hf://jfarray/Model_all-distilroberta-v1_30_Epochs@e063a3c85bae1d45e4eb9f8f7768d2169199638e/similarity_evaluation_sts-test_results.csv
-
curl -L -o similarity_evaluation_sts-test_results.csv https://huggingface.co/jfarray/Model_all-distilroberta-v1_30_Epochs/resolve/e063a3c85bae1d45e4eb9f8f7768d2169199638e/similarity_evaluation_sts-test_results.csv
302 Bytes
| epoch,steps,cosine_pearson,cosine_spearman,euclidean_pearson,euclidean_spearman,manhattan_pearson,manhattan_spearman,dot_pearson,dot_spearman | |
| -1,-1,0.7731994684889343,0.4264262412889712,0.7654176914768717,0.4264262412889712,0.7646561734925907,0.4314324312991191,0.7731994715770808,0.4264262412889712 | |