Instructions to use imamnurby/RoBERTduino_uncased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use imamnurby/RoBERTduino_uncased with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="imamnurby/RoBERTduino_uncased")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("imamnurby/RoBERTduino_uncased") model = AutoModel.from_pretrained("imamnurby/RoBERTduino_uncased", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7b7e60ab362f28544a929c406ce1bfe7814ab32bf6a31438ceeae690abda99b6
- Size of remote file:
- 499 MB
- SHA256:
- 9c28960e391c64cade4aa04ae0a5fabb7100b908cce3da162aa8d4bcc7199db6
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.