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{
"model_card": {
"Date & Time": "2024-08-05T13:02:15.447523",
"Model Card": [
"https://huggingface.co/FacebookAI/roberta-base"
],
"License Information": [
"mit"
],
"Citation Information": [
"\n@inproceedings{Wolf_Transformers_State-of-the-Art_Natural_2020,\n author = {Wolf, Thomas and Debut, Lysandre and Sanh, Victor and Chaumond, Julien",
"\n@Misc{peft,\n title = {PEFT: State-of-the-art Parameter-Efficient Fine-Tuning methods},\n author = {Sourab Mangrulkar and Sylvain Gugger and Lysandre Debut and Younes",
"@article{DBLP:journals/corr/abs-1907-11692,\n author = {Yinhan Liu and\n Myle Ott and\n Naman Goyal and\n Jingfei Du and\n Mandar Joshi and\n Danqi Chen and\n Omer Levy and\n Mike Lewis and\n Luke Zettlemoyer and\n Veselin Stoyanov},\n title = {RoBERTa: {A} Robustly Optimized {BERT} Pretraining Approach},\n journal = {CoRR},\n volume = {abs/1907.11692},\n year = {2019},\n url = {http://arxiv.org/abs/1907.11692},\n archivePrefix = {arXiv},\n eprint = {1907.11692},\n timestamp = {Thu, 01 Aug 2019 08:59:33 +0200},\n biburl = {https://dblp.org/rec/journals/corr/abs-1907-11692.bib},\n bibsource = {dblp computer science bibliography, https://dblp.org}\n}",
"@inproceedings{reimers-2019-sentence-bert,\n title = \"Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks\",\n author = \"Reimers, Nils and Gurevych, Iryna\",\n booktitle = \"Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing\",\n month = \"11\",\n year = \"2019\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://arxiv.org/abs/1908.10084\",\n}"
]
},
"data_card": {
"Get SynthSTEL Training Triplets Dataset": {
"Date & Time": "2024-07-22T12:32:49.982528",
"Dataset Name": [
"SynthSTEL/styledistance_training_triplets"
],
"Dataset Card": [
"https://huggingface.co/datasets/SynthSTEL/styledistance_training_triplets"
]
},
"Get SynthSTEL Training Triplets Dataset (train split)": {
"Date & Time": "2024-07-22T12:34:32.628286"
},
"Get SynthSTEL Training Triplets Dataset (train split) (shuffle)": {
"Date & Time": "2024-07-22T12:40:47.902534"
},
"Get SynthSTEL Training Triplets Dataset (train split) (shuffle) (take)": {
"Date & Time": "2024-07-22T12:40:53.004017"
},
"Get SynthSTEL Training Triplets Dataset (train split) (shuffle) (take) (select_columns)": {
"Date & Time": "2024-07-22T12:40:54.367439"
},
"concat(Get SynthSTEL Training Triplets Dataset (train split) (shuffle) (take) (select_columns), Get SynthSTEL Training Triplets Dataset #2 (take))": {
"Date & Time": "2024-07-22T12:43:44.056927"
},
"concat(Get SynthSTEL Training Triplets Dataset (train split) (shuffle) (take) (select_columns), Get SynthSTEL Training Triplets Dataset #2 (take)) (shuffle)": {
"Date & Time": "2024-07-23T14:22:55.032374"
}
},
"__version__": "0.35.0",
"datetime": "2024-07-23T14:22:55.632236",
"type": "TrainSentenceTransformer",
"name": "Train Wegmann + StyleDistance Model",
"version": 1.0,
"fingerprint": "620cd4c756865563",
"req_versions": {
"dill": "0.3.8",
"sqlitedict": "2.1.0",
"torch": "2.3.1",
"numpy": "1.26.4",
"transformers": "4.40.1",
"datasets": "2.17.0",
"huggingface_hub": "0.23.4",
"accelerate": "0.32.1",
"peft": "0.11.1",
"tiktoken": "0.7.0",
"tokenizers": "0.19.1",
"openai": "1.35.13",
"ctransformers": "0.2.27",
"optimum": "1.21.2",
"bitsandbytes": "0.43.1",
"litellm": "1.31.14",
"trl": "0.8.1",
"setfit": "1.0.3"
},
"interpreter": "3.10.9 (main, Apr 17 2023, 21:32:03) [GCC 7.5.0]"
} |