Text Classification
Transformers
PyTorch
TensorBoard
Safetensors
English
bert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use JeremiahZ/bert-base-uncased-stsb with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JeremiahZ/bert-base-uncased-stsb with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="JeremiahZ/bert-base-uncased-stsb")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("JeremiahZ/bert-base-uncased-stsb") model = AutoModelForSequenceClassification.from_pretrained("JeremiahZ/bert-base-uncased-stsb") - 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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- glue
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metrics:
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- spearmanr
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model-index:
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- name: bert-base-uncased-stsb
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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 STSB
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type: glue
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args: stsb
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metrics:
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type: spearmanr
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value: 0.8843169724798454
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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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- glue
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metrics:
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- spearmanr
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base_model: bert-base-uncased
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model-index:
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- name: bert-base-uncased-stsb
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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 STSB
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type: glue
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args: stsb
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metrics:
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- type: spearmanr
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value: 0.8843169724798454
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name: Spearmanr
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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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