Sentence Similarity
sentence-transformers
PyTorch
Safetensors
Transformers
Greek
bert
feature-extraction
Eval Results (legacy)
text-embeddings-inference
Instructions to use dimitriz/st-greek-media-bert-base-uncased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use dimitriz/st-greek-media-bert-base-uncased with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("dimitriz/st-greek-media-bert-base-uncased") 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] - Transformers
How to use dimitriz/st-greek-media-bert-base-uncased with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("dimitriz/st-greek-media-bert-base-uncased") model = AutoModel.from_pretrained("dimitriz/st-greek-media-bert-base-uncased") - Notebooks
- Google Colab
- Kaggle
| epoch,steps,accuracy_cosinus,accuracy_manhattan,accuracy_euclidean | |
| 0,1000,0.7708083910259746,0.7698714278278069,0.7704960699599188 | |
| 0,2000,0.8108375409921399,0.8136310794163066,0.8141342633560633 | |
| 0,3000,0.8408897680148526,0.8418961358943661,0.8424166710044593 | |
| 0,4000,0.8605312928357017,0.8604792393246925,0.8606874533687298 | |
| 0,5000,0.8751930317699929,0.8756268110284039,0.875748269220759 | |
| 0,6000,0.8834695400204744,0.8842503426856142,0.8839553727898947 | |
| 0,7000,0.8923706904030677,0.8926656602987871,0.8926309579581143 | |
| 0,8000,0.8975413391633266,0.897714850866691,0.8978016067183732 | |
| 0,9000,0.9009248173789323,0.901983238769455,0.9020526434508007 | |
| 0,10000,0.9048461818749675,0.9053146634740513,0.9053493658147242 | |
| 0,-1,0.9095483490361425,0.9099994794648899,0.9103812052122916 | |
| 1,1000,0.9107976333003661,0.9109190914927212,0.9110405496850763 | |
| 1,2000,0.9121857269272813,0.912255131608627,0.9129491784220846 | |
| 1,3000,0.9159335797199522,0.9166102753630733,0.9167317335554284 | |
| 1,4000,0.9201499141117069,0.9200458070896882,0.9203234258150712 | |
| 1,5000,0.9212950913539119,0.9217982752936685,0.9219197334860236 | |
| 1,6000,0.9216421147606406,0.9218503288046779,0.9219891381673694 | |
| 1,7000,0.9259278538337411,0.9261013655371054,0.9262575260701335 | |
| 1,8000,0.9295889507747298,0.9296063019450662,0.9296236531154026 | |
| 1,9000,0.9333715059080735,0.9337011781444658,0.9337185293148023 | |
| 1,10000,0.9384727499869866,0.9389585827564069,0.9390106362674162 | |
| 1,-1,0.9391494456301077,0.9394444155258272,0.9396873319105373 | |
| 2,1000,0.9392014991411171,0.9394097131851543,0.9397046830808738 | |
| 2,2000,0.942081793416966,0.9421338469279753,0.9422726562906668 | |
| 2,3000,0.9455173251435809,0.9454305692918987,0.9456214321655996 | |
| 2,4000,0.9474433050509257,0.9475821144136172,0.9477209237763087 | |
| 2,5000,0.9499245224090365,0.9500980341124009,0.9503583016674475 | |
| 2,6000,0.9523883885968109,0.9523189839154651,0.9525271979595024 | |
| 2,7000,0.9531171377509413,0.9530824354102684,0.953342702965315 | |
| 2,8000,0.9544358266965107,0.9545225825481929,0.9545919872295386 | |
| 2,9000,0.9547307965922301,0.9550778199989589,0.9551298735099683 | |
| 2,10000,0.9560494855377996,0.9562403484115004,0.9563097530928462 | |
| 2,-1,0.9563965089445283,0.9565353183072198,0.9566394253292384 | |