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Update datadreamer.json

datadreamer.json

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+{
+    "model_card": {
+        "Date & Time": "2024-07-21T15:59:10.187730",
+        "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": "2024-07-21T13:01:08.536821",
+            "Dataset Name": [
+                "SynthSTEL/styledistance_training_triplets_v2"
+            ],
+            "Dataset Card": [
+                "https://huggingface.co/datasets/SynthSTEL/styledistance_training_triplets_v2"
+            ]
+        },
+        "Get SynthSTEL Training Triplets Dataset (train split)": {
+            "Date & Time": "2024-07-21T13:03:10.150361"
+        },
+        "Get SynthSTEL Training Triplets Dataset (train split) (shuffle)": {
+            "Date & Time": "2024-07-21T15:20:06.418759"
+        }
+    },
+    "__version__": "0.35.0",
+    "datetime": "2024-07-21T15:20:07.042657",
+    "type": "TrainSentenceTransformer",
+    "name": "Train StyleDistance Model",
+    "version": 1.0,
+    "fingerprint": "b5d8928303367cc0",
+    "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]"
+}