Text Classification
Transformers
TensorBoard
Safetensors
bert
HHD
10_class
multi_labels
Generated from Trainer
text-embeddings-inference
Instructions to use ccs2/model_output with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ccs2/model_output with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ccs2/model_output")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ccs2/model_output") model = AutoModelForSequenceClassification.from_pretrained("ccs2/model_output", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0e6559447e353c927797a692f1dea2e03d1f2f3fc9694d4137d5ef808b71e33f
- Size of remote file:
- 5.18 kB
- SHA256:
- f926b3abd1ad68800340de007b60ec230bee63eb6c17dfc34e1d8711d1346592
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