Text Classification
Transformers
PyTorch
English
bert
Eval Results (legacy)
text-embeddings-inference
Instructions to use MattStammers/Statement_Equivalence with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MattStammers/Statement_Equivalence with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="MattStammers/Statement_Equivalence")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("MattStammers/Statement_Equivalence") model = AutoModelForSequenceClassification.from_pretrained("MattStammers/Statement_Equivalence", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit ·
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Parent(s): b842aef
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README.md
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model_id: test_trainer
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developers: Matt Stammers
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model_type: BERT-Base-Uncased
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model-index:
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- name: test_trainer
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results:
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<!-- Provide a quick summary of what the model is/does. -->
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## Model Details
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model_id: test_trainer
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developers: Matt Stammers
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model_type: BERT-Base-Uncased
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model_summary: This model Compares the similarity of two text objects
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model-index:
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- name: test_trainer
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results:
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<!-- Provide a quick summary of what the model is/does. -->
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This model Compares the similarity of two text objects
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## Model Details
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