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 ·
94d13e5
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Parent(s): b68e858
Upload README.md with huggingface_hub
Browse files
README.md
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@@ -47,7 +47,6 @@ glossary: N/A
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more_information: Can be made available on request
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model_card_authors: Matt Stammers
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model_card_contact: Matt Stammers
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pipeline_tag: text-classification
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model-index:
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- name: test_trainer
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results:
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type: mrcp
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metrics:
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- type: accuracy
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value: 0.
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- type: F1-Score
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value: 0.
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---
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# Model Card for test_trainer
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more_information: Can be made available on request
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model_card_authors: Matt Stammers
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model_card_contact: Matt Stammers
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model-index:
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- name: test_trainer
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results:
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type: mrcp
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metrics:
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- type: accuracy
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value: 0.8480392156862745
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- type: F1-Score
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value: 0.8945578231292517
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---
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# Model Card for test_trainer
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