Sentence Similarity
sentence-transformers
Safetensors
modernbert
feature-extraction
dense
Generated from Trainer
dataset_size:8118
loss:CachedMultipleNegativesRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use benjamintli/modernbert-cosqa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use benjamintli/modernbert-cosqa with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("benjamintli/modernbert-cosqa") sentences = [ "python create path if doesnt exist", "def clean_whitespace(string, compact=False):\n \"\"\"Return string with compressed whitespace.\"\"\"\n for a, b in (('\\r\\n', '\\n'), ('\\r', '\\n'), ('\\n\\n', '\\n'),\n ('\\t', ' '), (' ', ' ')):\n string = string.replace(a, b)\n if compact:\n for a, b in (('\\n', ' '), ('[ ', '['),\n (' ', ' '), (' ', ' '), (' ', ' ')):\n string = string.replace(a, b)\n return string.strip()", "def rotateImage(img, angle):\n \"\"\"\n\n querries scipy.ndimage.rotate routine\n :param img: image to be rotated\n :param angle: angle to be rotated (radian)\n :return: rotated image\n \"\"\"\n imgR = scipy.ndimage.rotate(img, angle, reshape=False)\n return imgR", "def check_create_folder(filename):\n \"\"\"Check if the folder exisits. If not, create the folder\"\"\"\n os.makedirs(os.path.dirname(filename), exist_ok=True)" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
| epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accuracy@10,cosine-Precision@1,cosine-Recall@1,cosine-Precision@3,cosine-Recall@3,cosine-Precision@5,cosine-Recall@5,cosine-Precision@10,cosine-Recall@10,cosine-MRR@10,cosine-NDCG@10,cosine-MAP@100 | |
| 1.0,8,0.6130820399113082,0.8802660753880266,0.9390243902439024,0.9733924611973392,0.6130820399113082,0.6130820399113082,0.2934220251293422,0.8802660753880266,0.18780487804878046,0.9390243902439024,0.09733924611973392,0.9733924611973392,0.7519726885580548,0.8070706362163947,0.7532799330596042 | |
| 2.0,16,0.623059866962306,0.8813747228381374,0.9368070953436807,0.9733924611973392,0.623059866962306,0.623059866962306,0.29379157427937913,0.8813747228381374,0.18736141906873613,0.9368070953436807,0.09733924611973392,0.9733924611973392,0.7572013690916134,0.8109634382854236,0.7585413772793764 | |
| 3.0,24,0.6252771618625277,0.8835920177383592,0.9390243902439024,0.9745011086474501,0.6252771618625277,0.6252771618625277,0.29453067257945303,0.8835920177383592,0.1878048780487805,0.9390243902439024,0.09745011086474502,0.9745011086474501,0.759677348396861,0.8131406204702663,0.7609307194216963 | |
| 4.0,32,0.6241685144124168,0.8835920177383592,0.9401330376940134,0.975609756097561,0.6241685144124168,0.6241685144124168,0.29453067257945303,0.8835920177383592,0.18802660753880268,0.9401330376940134,0.0975609756097561,0.975609756097561,0.7599637489881396,0.8136213487451881,0.7611712149645016 | |
| 5.0,40,0.6252771618625277,0.88470066518847,0.9390243902439024,0.9767184035476718,0.6252771618625277,0.6252771618625277,0.29490022172949004,0.88470066518847,0.1878048780487805,0.9390243902439024,0.09767184035476718,0.9767184035476718,0.7611507056629011,0.8147692547528804,0.7622636042011719 | |
| 6.0,48,0.6175166297117517,0.885809312638581,0.9390243902439024,0.9767184035476718,0.6175166297117517,0.6175166297117517,0.295269770879527,0.885809312638581,0.1878048780487805,0.9390243902439024,0.09767184035476718,0.9767184035476718,0.7573271917784116,0.811953153225869,0.7584708896043009 | |
| 7.0,56,0.6208425720620843,0.8824833702882483,0.9401330376940134,0.9778270509977827,0.6208425720620843,0.6208425720620843,0.29416112342941614,0.8824833702882483,0.18802660753880265,0.9401330376940134,0.0977827050997783,0.9778270509977827,0.7585603420969276,0.8130799896667618,0.7596029461097283 | |
| 8.0,64,0.6208425720620843,0.8824833702882483,0.9401330376940134,0.9767184035476718,0.6208425720620843,0.6208425720620843,0.2941611234294161,0.8824833702882483,0.18802660753880265,0.9401330376940134,0.09767184035476718,0.9767184035476718,0.7584028437686978,0.8127114359601555,0.759548534316922 | |
| 9.0,72,0.6130820399113082,0.885809312638581,0.9390243902439024,0.9778270509977827,0.6130820399113082,0.6130820399113082,0.295269770879527,0.885809312638581,0.1878048780487805,0.9390243902439024,0.0977827050997783,0.9778270509977827,0.7551160560306903,0.8105669095191763,0.7561685918100882 | |
| 10.0,80,0.6197339246119734,0.88470066518847,0.9390243902439024,0.9778270509977827,0.6197339246119734,0.6197339246119734,0.29490022172949004,0.88470066518847,0.18780487804878046,0.9390243902439024,0.0977827050997783,0.9778270509977827,0.7577473339668463,0.8124675617500997,0.7588050805217604 | |