Text Classification
Transformers
Safetensors
distilbert
pytorch_model_hub_mixin
model_hub_mixin
custom_code
text-embeddings-inference
Instructions to use cshin23/multidim-rm_reg_gating_prototype with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cshin23/multidim-rm_reg_gating_prototype with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="cshin23/multidim-rm_reg_gating_prototype", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("cshin23/multidim-rm_reg_gating_prototype", trust_remote_code=True) model = AutoModelForSequenceClassification.from_pretrained("cshin23/multidim-rm_reg_gating_prototype", trust_remote_code=True) - Notebooks
- Google Colab
- Kaggle
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("cshin23/multidim-rm_reg_gating_prototype", trust_remote_code=True)
model = AutoModelForSequenceClassification.from_pretrained("cshin23/multidim-rm_reg_gating_prototype", trust_remote_code=True)Quick Links
This model has been pushed to the Hub using the PytorchModelHubMixin integration:
- Library: [More Information Needed]
- Docs: [More Information Needed]
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="cshin23/multidim-rm_reg_gating_prototype", trust_remote_code=True)