Text Classification
Transformers
Safetensors
English
bert
nlp
routing
vision-task-classifier
text-embeddings-inference
Instructions to use beingamanforever/ICM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use beingamanforever/ICM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="beingamanforever/ICM")# Load model directly from transformers import AutoTokenizer, TaskClassifier tokenizer = AutoTokenizer.from_pretrained("beingamanforever/ICM") model = TaskClassifier.from_pretrained("beingamanforever/ICM") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 79767d283649efa9f248f17d76a0af8ad097fdb68888c0cf9e29c2ae6aa7373d
- Size of remote file:
- 5.84 kB
- SHA256:
- b88d4bf5ff29db3aa978d322dd0c84e23f85369960fe3635abe7d0e92bd27b53
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.