Text Classification
Transformers
PyTorch
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use kimsiun/ec_classfication_0502_distilbert_base_uncased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kimsiun/ec_classfication_0502_distilbert_base_uncased with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="kimsiun/ec_classfication_0502_distilbert_base_uncased")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("kimsiun/ec_classfication_0502_distilbert_base_uncased") model = AutoModelForSequenceClassification.from_pretrained("kimsiun/ec_classfication_0502_distilbert_base_uncased") - Notebooks
- Google Colab
- Kaggle
Adding `safetensors` variant of this model
#1
by SFconvertbot - opened
- model.safetensors +3 -0
model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:4925f990ccf8b7018c7fdf6126feb666d82096581ad6d463e918248751bc1b55
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size 267832560
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