Text Classification
Transformers
TensorBoard
Safetensors
bert
ley
categorical
multi_label
10_class
Generated from Trainer
text-embeddings-inference
Instructions to use eunyoung2/model_output with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use eunyoung2/model_output with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="eunyoung2/model_output")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("eunyoung2/model_output") model = AutoModelForSequenceClassification.from_pretrained("eunyoung2/model_output") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ed6dcce6791ca31471e0057416bfd2b86ebc61af07eaa64559d7f741507ff94c
- Size of remote file:
- 436 MB
- SHA256:
- dc1cb433902499a0bc9a9c50558b87905aaa5c136f9e60b13c9d166c059dc08c
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