Instructions to use YakovElm/MariaDB_10_RoBERTa_Under_Sampling with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use YakovElm/MariaDB_10_RoBERTa_Under_Sampling with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="YakovElm/MariaDB_10_RoBERTa_Under_Sampling")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("YakovElm/MariaDB_10_RoBERTa_Under_Sampling") model = AutoModelForSequenceClassification.from_pretrained("YakovElm/MariaDB_10_RoBERTa_Under_Sampling", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from YakovElm/MariaDB_10_RoBERTa_Under_Sampling: direct link, hf CLI and curl.
- Browser
- Download file 499 MB
-
https://huggingface.co/YakovElm/MariaDB_10_RoBERTa_Under_Sampling/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://YakovElm/MariaDB_10_RoBERTa_Under_Sampling/pytorch_model.bin
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curl -L -o pytorch_model.bin https://huggingface.co/YakovElm/MariaDB_10_RoBERTa_Under_Sampling/resolve/main/pytorch_model.bin
499 MB
- Xet hash:
- e98ac6f9d07589703712f929466366da5fbe5428e157b404d9f826a85a3c505f
- Size of remote file:
- 499 MB
- SHA256:
- e847a0e9880e9309162addfc90d28ea5a44c8709a214a733da3842e0abc40e2c
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