Text Classification
Transformers
Safetensors
modernbert
feature-extraction
doom
game-ai
ascii
ModernBERT
hash-embeddings
depth-aware
attention-pooling
classifier
real-time
edge-deployment
tiny-model
custom_code
text-embeddings-inference
Instructions to use VAGOsolutions/SauerkrautLM-Doom-MultiVec-1.3M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use VAGOsolutions/SauerkrautLM-Doom-MultiVec-1.3M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="VAGOsolutions/SauerkrautLM-Doom-MultiVec-1.3M", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("VAGOsolutions/SauerkrautLM-Doom-MultiVec-1.3M", trust_remote_code=True) model = AutoModel.from_pretrained("VAGOsolutions/SauerkrautLM-Doom-MultiVec-1.3M", trust_remote_code=True) - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 015acf65ab8c5f6f3b60484bb4a40b4e75547196a57f6be3ca2c859c781bac22
- Size of remote file:
- 5.29 MB
- SHA256:
- 9da8a51d1fabf56de52772a618701cdc7c079be429664026a111aee1d9039a16
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