Text Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
text-embeddings-inference
Instructions to use ksmcg/name with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ksmcg/name with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ksmcg/name")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ksmcg/name") model = AutoModelForSequenceClassification.from_pretrained("ksmcg/name") - Notebooks
- Google Colab
- Kaggle
Librarian Bot: Add base_model information to model
#2
by librarian-bot - opened
README.md
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name: glue
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type: glue
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args: mrpc
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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name: glue
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type: glue
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args: mrpc
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base_model: bert-base-uncased
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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