Instructions to use rifkat/pubchem_1M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rifkat/pubchem_1M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="rifkat/pubchem_1M")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("rifkat/pubchem_1M") model = AutoModelForMaskedLM.from_pretrained("rifkat/pubchem_1M") - Notebooks
- Google Colab
- Kaggle
YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
Ushbu model, HuggingFace-da RoBERTa transformatorini amalga oshirishga asoslangan. Bizning RoBERTa dasturimiz 12 ta diqqat boshi va 6 ta qatlamdan foydalanadi, natijada 72 ta aniq e'tibor mexanizmlari paydo bo'ladi. Biz har bir kirish satridagi tokenlarning 15 foizini niqoblaydigan RoBERTa-dan dastlabki tekshirish protsedurasini qabul qildik. Biz maksimal 52K tokenli lug'atdan va maksimal 512 ta ketma-ketlik uzunligidan foydalanganmiz. Biz 1M PubChem to'plamlarida 10 ta davr uchun o'qitdik. Loss funksiya 2.9 dan 0.33 gacha tushdi. Ushbu modelni sizga taqdim qilamiz.
@misc {rifkat_davronov_2022,
author = { {Adilova Fatima,Rifkat Davronov, Samariddin Kushmuratov, Ruzmat Safarov} },
title = { pubchem_1M (Revision 89e2ba6) },
year = 2022,
url = { https://huggingface.co/rifkat/pubchem_1M },
doi = { 10.57967/hf/0177 },
publisher = { Hugging Face }
}
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