Text Classification
Transformers
TensorBoard
Safetensors
bert
HHD
10_class
multi_labels
Generated from Trainer
text-embeddings-inference
Instructions to use AmPlar/model_output with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AmPlar/model_output with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="AmPlar/model_output")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("AmPlar/model_output") model = AutoModelForSequenceClassification.from_pretrained("AmPlar/model_output", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 12ea2020f693a8f8e8292564af44f665cc5ed72fec85cfbb934b0080b872ca22
- Size of remote file:
- 436 MB
- SHA256:
- feae42cb9dc33643148db921285c5795dd9c234ce7545610094e53cc8da02057
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