Text Classification
Transformers
PyTorch
TensorBoard
English
bert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use ljh1/mrpc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ljh1/mrpc with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ljh1/mrpc")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ljh1/mrpc") model = AutoModelForSequenceClassification.from_pretrained("ljh1/mrpc") - Notebooks
- Google Colab
- Kaggle
Librarian Bot: Add base_model information to model
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by librarian-bot - opened
README.md
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metrics:
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- accuracy
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- f1
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model-index:
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- name: mrpc
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results:
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- task:
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name: Text Classification
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type: text-classification
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dataset:
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name: GLUE MRPC
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type: glue
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split: validation
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args: mrpc
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metrics:
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type: accuracy
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value: 0.6911764705882353
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value: 0.8157894736842105
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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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metrics:
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- accuracy
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- f1
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base_model: bert-base-cased
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model-index:
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- name: mrpc
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results:
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- task:
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type: text-classification
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name: Text Classification
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dataset:
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name: GLUE MRPC
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type: glue
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split: validation
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args: mrpc
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metrics:
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- type: accuracy
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value: 0.6911764705882353
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name: Accuracy
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- type: f1
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value: 0.8157894736842105
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name: F1
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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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