Text Classification
Transformers
PyTorch
TensorBoard
Safetensors
English
bert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use JeremiahZ/bert-base-uncased-mrpc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JeremiahZ/bert-base-uncased-mrpc with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="JeremiahZ/bert-base-uncased-mrpc")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("JeremiahZ/bert-base-uncased-mrpc") model = AutoModelForSequenceClassification.from_pretrained("JeremiahZ/bert-base-uncased-mrpc") - Notebooks
- Google Colab
- Kaggle
Jeremiah Zhou commited on
Commit ·
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Parent(s): 08705fe
update model card README.md
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README.md
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---
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license: apache-2.0
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tags:
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name: Text Classification
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type: text-classification
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dataset:
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name:
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type: glue
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args: mrpc
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metrics:
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- name: Accuracy
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type: accuracy
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value: 0.
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type: 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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# bert-base-uncased-mrpc
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This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the
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It achieves the following results on the evaluation set:
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- Accuracy: 0.
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## Model description
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---
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language:
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license: apache-2.0
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tags:
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- generated_from_trainer
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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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args: mrpc
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metrics:
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- name: Accuracy
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type: accuracy
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value: 0.8578431372549019
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- name: F1
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type: f1
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value: 0.9023569023569024
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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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# bert-base-uncased-mrpc
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This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the GLUE MRPC dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.5572
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- Accuracy: 0.8578
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- F1: 0.9024
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- Combined Score: 0.8801
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## Model description
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