Model Card for Statement_Equivalence

<!-- Provide a quick summary of what the model is/does. -->

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.

Model Details

Model Description

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• Developed by: Matt Stammers
• Shared by [optional]: Matt Stammers
• Model type: BERT-Base-Uncased
• Language(s) (NLP): en
• License: mit
• Finetuned from model [optional]: Glue

Model Sources [optional]

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• Repository: https://huggingface.co/MattStammers/Statement_Equivalence?text=I+like+you.+I+love+you
• Paper [optional]: N/A
• Demo [optional]: N/A

Uses

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Direct Use

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Test it out here

Downstream Use [optional]

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This is a standalone app

Out-of-Scope Use

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The model will not work with any very complex sentences or to compare more than 3 statements

Bias, Risks, and Limitations

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Biases inherent in Glue also apply here

Recommendations

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Do not be surprised if unusual results are obtained

How to Get Started with the Model

Use the code below to get started with the model.

``` python 
# 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")
```

Training Details

Training Data

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See Glue Dataset: https://huggingface.co/datasets/glue

Training Procedure

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Preprocessing [optional]

Sentence Pairs to analyse similarity

Training Hyperparameters

• Training regime: User Defined <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

Speeds, Sizes, Times [optional]

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Not Relevant

Evaluation

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Testing Data, Factors & Metrics

Testing Data

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MRCP. Link: https://huggingface.co/datasets/SetFit/mrpc

Factors

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N/A

Metrics

<!-- These are the evaluation metrics being used, ideally with a description of why. -->

N/A

Results

See evaluation results.

Summary

See Over

Model Examination [optional]

<!-- Relevant interpretability work for the model goes here -->

Model should be interpreted with user discretion.

Environmental Impact

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Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

• Hardware Type: T600
• Hours used: 0.1
• Cloud Provider: N/A
• Compute Region: N/A
• Carbon Emitted: <1

Technical Specifications [optional]

Model Architecture and Objective

Bert fine-tuned

Compute Infrastructure

requires less than 4GB of GPU to run quickly

Hardware

T600

Software

Python, pytorch with transformers

Citation [optional]

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BibTeX:

N/A

APA:

N/A

Glossary [optional]

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N/A

More Information [optional]

Can be made available on request

Model Card Authors [optional]

Matt Stammers

Model Card Contact

Matt Stammers