Text Classification
Transformers
Safetensors
English
modernbert
memory
darkmem
text-embeddings-inference
Instructions to use darkraise/darkmem-classifier-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use darkraise/darkmem-classifier-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="darkraise/darkmem-classifier-v1")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("darkraise/darkmem-classifier-v1") model = AutoModelForSequenceClassification.from_pretrained("darkraise/darkmem-classifier-v1") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 101f045118970d08fbc241d5dfd303b99d0d644291570147907ca00dacdbdaac
- Size of remote file:
- 5.2 kB
- SHA256:
- b17ceed1e47a915fbae3cdc7a2539d31307dc7fbda36843a34b7dbb0ac887a35
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.