Instructions to use RogerKam/subTopics-RoBERTa-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RogerKam/subTopics-RoBERTa-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="RogerKam/subTopics-RoBERTa-base")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("RogerKam/subTopics-RoBERTa-base") model = AutoModelForSequenceClassification.from_pretrained("RogerKam/subTopics-RoBERTa-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from RogerKam/subTopics-RoBERTa-base: direct link, hf CLI and curl.
- Browser
- Download file 499 MB
-
https://huggingface.co/RogerKam/subTopics-RoBERTa-base/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://RogerKam/subTopics-RoBERTa-base/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/RogerKam/subTopics-RoBERTa-base/resolve/main/pytorch_model.bin
499 MB
- Xet hash:
- e703090dd082136f11ecb0182e1bc30cb2abf67ed2077c17d9bedbecd40c20a9
- Size of remote file:
- 499 MB
- SHA256:
- 5cc7f70771df7fe242237b5a181748778292b0c4cd95e9a5ba7d5bdf10528f64
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