Model: MattStammers/Statement_Equivalence
Author: MattStammers
Pipeline: text-classification
Library: transformers
Tags: transformers, pytorch, bert, text-classification, en, arxiv:1910.09700, license:mit, model-index, endpoints_compatible, region:us
Card metadata: language: en
license: mit
model_id: Statement_Equivalence
developers: Matt Stammers
model_type: BERT-Base-Uncased
model_summary: 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.
shared_by: Matt Stammers
finetuned_from: Glue
repo: https://huggingface.co/MattStammers/Statement_Equivalence?text=I+like+you.+I+love+you
paper: N/A
demo: N/A
direct_use: Test it out here
downstream_use: This is a standalone app
out_of_scope_use: The model will not work with any very complex sentences or to compare more than 3 statements
bias_risks_limitations: Biases inherent in Glue also apply here
bias_recommendations: Do not be surprised if unusual results are obtained
get_started_code: 
    ``` 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_data: See Glue Dataset: https://huggingface.co/datasets/glue
preprocessing: Sentence Pairs to analyse similarity
training_regime: User Defined
speeds_sizes_times: Not Relevant
testing_data: MRCP. Link: https://huggingface.co/datasets/SetFit/mrpc
testing_factors: N/A
testing_metrics: N/A
results: See evaluation results.
results_summary: See Over
model_examination: Model should be interpreted with user discretion.
model_specs: Bert fine-tuned
compute_infrastructure: requires less than 4GB of GPU to run quickly
hardware: T600
hours_used: 0.1
cloud_provider: N/A
cloud_region: N/A
co2_emitted: <1
software: Python, pytorch with transformers
citation_bibtex: N/A
citation_apa: N/A
glossary: N/A
more_information: Can be made available on request
model_card_authors: Matt Stammers
model_card_contact: Matt Stammers
model-index: name: statement
results: task: type: text-classification
dataset: name: MRCP
type: mrcp
metrics: type: accuracy
value: 0.8480392156862745
verified: no, type: F1-Score
value: 0.8945578231292517
verified: no
Likes: 0 | All-time downloads: 496
Created: 2023-09-28 20:02:32