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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4180d39db455a372c289cc8eddc43cbb6561dfc50f357c324dbd77dd402b4fdb
- Size of remote file:
- 5.18 kB
- SHA256:
- b70fe1b9479012c0d78e839f68b7457d723afe0cda699505ffbb24606be2b057
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