Instructions to use cl-nagoya/sup-simcse-ja-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use cl-nagoya/sup-simcse-ja-large with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("cl-nagoya/sup-simcse-ja-large") 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 cl-nagoya/sup-simcse-ja-large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="cl-nagoya/sup-simcse-ja-large")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("cl-nagoya/sup-simcse-ja-large") model = AutoModel.from_pretrained("cl-nagoya/sup-simcse-ja-large", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download log.csv from cl-nagoya/sup-simcse-ja-large: direct link, hf CLI and curl.
- Browser
- Download file 2.54 kB
-
https://huggingface.co/cl-nagoya/sup-simcse-ja-large/resolve/main/log.csv
- Command line
-
hf download hf://cl-nagoya/sup-simcse-ja-large/log.csv
-
curl -L -o log.csv https://huggingface.co/cl-nagoya/sup-simcse-ja-large/resolve/main/log.csv
2.54 kB
| epoch,step,loss,sts-dev | |
| 0,0,inf,46.72837224312097 | |
| 0,32,5.49169921875,79.31070160603963 | |
| 0,64,2.51708984375,84.35700372236889 | |
| 0,96,2.013916015625,82.41349585392163 | |
| 0,128,1.83056640625,80.40566982305087 | |
| 0,160,1.715576171875,77.84721366215935 | |
| 0,192,1.650146484375,78.6757653419235 | |
| 0,224,1.568115234375,73.42996624180068 | |
| 0,256,1.55322265625,75.57552162351263 | |
| 1,288,1.490966796875,71.41986682440324 | |
| 1,320,1.37548828125,73.29193104300393 | |
| 1,352,1.33544921875,72.80806281746437 | |
| 1,384,1.350830078125,72.77195292066885 | |
| 1,416,1.353759765625,69.87782142691363 | |
| 1,448,1.33447265625,64.9175102353748 | |
| 1,480,1.309326171875,69.45867633288623 | |
| 1,512,1.31982421875,70.6730909328148 | |
| 1,544,1.29150390625,70.64751383186746 | |
| 2,576,1.212646484375,65.82802791174757 | |
| 2,608,1.11669921875,66.64306435617323 | |
| 2,640,1.14453125,65.48764039746843 | |
| 2,672,1.1185302734375,66.58600028071007 | |
| 2,704,1.123779296875,67.64014437611549 | |
| 2,736,1.103759765625,69.60152425618847 | |
| 2,768,1.141357421875,69.34622506400544 | |
| 2,800,1.270263671875,70.32409725193519 | |
| 2,832,1.221435546875,67.92858244662744 | |
| 3,864,1.0614013671875,66.34717772523808 | |
| 3,896,1.0054931640625,66.87521082007706 | |
| 3,928,1.0040283203125,65.41458032481682 | |
| 3,960,1.0062255859375,65.70913894370779 | |
| 3,992,1.0181884765625,66.46946316417328 | |
| 3,1024,0.996337890625,63.34887069413857 | |
| 3,1056,1.02392578125,65.18104616486384 | |
| 3,1088,0.9970703125,64.49234957916627 | |
| 3,1120,1.0,64.80023992366682 | |
| 4,1152,0.884521484375,65.09789161785304 | |
| 4,1184,0.880615234375,66.50805762020076 | |
| 4,1216,0.886474609375,63.74224407082551 | |
| 4,1248,0.8818359375,63.54117456035738 | |
| 4,1280,0.8819580078125,65.77741134837758 | |
| 4,1312,0.883544921875,66.75449263267615 | |
| 4,1344,0.875732421875,63.99539291809667 | |
| 4,1376,0.9068603515625,64.31698278731722 | |
| 5,1408,0.8865966796875,65.00043435754733 | |
| 5,1440,0.7822265625,64.22602226609517 | |
| 5,1472,0.785400390625,64.0694313946185 | |
| 5,1504,0.7928466796875,63.40049229004234 | |
| 5,1536,0.784912109375,62.86333382743011 | |
| 5,1568,0.794189453125,64.06125094235347 | |
| 5,1600,0.810546875,62.99456391252577 | |
| 5,1632,0.8006591796875,62.3648377584635 | |
| 5,1664,0.7896728515625,64.00460895931644 | |
| 6,1696,0.7900390625,64.12996526496609 | |
| 6,1728,0.720703125,64.04989699764432 | |
| 6,1760,0.7353515625,62.60633883039293 | |
| 6,1792,0.735595703125,62.39235079765329 | |
| 6,1824,0.72314453125,62.836391633071166 | |
| 6,1856,0.7305908203125,63.51677689636537 | |
| 6,1888,0.729248046875,63.40423279523494 | |
| 6,1920,0.7313232421875,63.03712185206447 | |
| 6,1952,0.733154296875,63.04729553320853 | |
| 7,1984,0.699462890625,62.68467625961607 | |
| 7,2016,0.70556640625,62.67148071717765 | |
| 7,2048,0.69970703125,62.75529368448359 | |