Instructions to use DimitriosPanagoulias/MemoryBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DimitriosPanagoulias/MemoryBERT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="DimitriosPanagoulias/MemoryBERT")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("DimitriosPanagoulias/MemoryBERT") model = AutoModelForSequenceClassification.from_pretrained("DimitriosPanagoulias/MemoryBERT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 30db36553fc80d4a52f7d425b96866cf7bc2b8d3ddec29100f85045ca2f2e9aa
- Size of remote file:
- 499 MB
- SHA256:
- c55934c2244a51df2a5cf8ac35b6f9007a3a3f9438405c5030ae327efd2b2e81
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.