Text Classification
Transformers
PyTorch
TensorBoard
Safetensors
English
roberta
text-embeddings-inference
Instructions to use smeintadmin/image_intents with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use smeintadmin/image_intents with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="smeintadmin/image_intents")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("smeintadmin/image_intents") model = AutoModelForSequenceClassification.from_pretrained("smeintadmin/image_intents", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 146d2d17f794d5cb73a051e2afbf07f333d3258a979f77da5b32ed9adbc814ec
- Size of remote file:
- 3.9 kB
- SHA256:
- 8434cb556cc1edbd48d4ac1a3f3ec0b1e09492c025297d4c4ef93d53462acec8
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.