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")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("MattStammers/Statement_Equivalence") model = AutoModelForSequenceClassification.from_pretrained("MattStammers/Statement_Equivalence") - Notebooks
- Google Colab
- Kaggle
Commit ·
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Parent(s): c68fce5
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README.md
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model_id: Statement_Equivalence
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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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shared_by: Matt Stammers
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finetuned_from: Glue
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repo: https://huggingface.co/MattStammers/Statement_Equivalence?text=I+like+you.+I+love+you
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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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model_id: Statement_Equivalence
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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. It is the first
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BERT model I have fine tuned so there may be bugs. The model labels should read
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equivalent/not-equivalent but despite mapping the id2label variables they are presently
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still displaying as label0/1 in the inference module. I may come back and fix this
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at a later date.
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shared_by: Matt Stammers
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finetuned_from: Glue
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repo: https://huggingface.co/MattStammers/Statement_Equivalence?text=I+like+you.+I+love+you
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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. It is the first BERT model I have fine tuned so there may be bugs. The model labels should read equivalent/not-equivalent but despite mapping the id2label variables they are presently still displaying as label0/1 in the inference module. I may come back and fix this at a later date.
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## Model Details
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