Text Classification
Transformers
TensorBoard
Safetensors
bert
jyp
categorical
multi_label
10_class
Generated from Trainer
text-embeddings-inference
Instructions to use GGami/model_output with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use GGami/model_output with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="GGami/model_output")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("GGami/model_output") model = AutoModelForSequenceClassification.from_pretrained("GGami/model_output", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 029b1c971a60ac8bad875d435fb0de99d109317206722d9d1257163587300992
- Size of remote file:
- 436 MB
- SHA256:
- 09da07a59c9902902d57fed1306e31e3649d32faee3dfa627cde510eadfa67d4
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