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:
- dd014647210efede46e366e7cf0e744f0a95dbed2cc471a9a27c76bf18b6a767
- Size of remote file:
- 7.89 kB
- SHA256:
- bfbb3448c8102892a8642f175c67a2c2d780f36174fe94d71c7a0368776e03ae
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.