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  ---
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- license: mit
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  tags:
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- - generated_from_trainer
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  metrics:
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  - accuracy
 
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  model-index:
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  - name: roberta-large-cased-en-cola_32_2e-05_lr_0.0001_decay_balanced
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  results: []
 
 
 
 
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  ---
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- <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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- should probably proofread and complete it, then remove this comment. -->
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- # roberta-large-cased-en-cola_32_2e-05_lr_0.0001_decay_balanced
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- This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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  - Loss: 0.6823
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  - Accuracy: 0.8729
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  - Mcc: 0.6922
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- ## Model description
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- More information needed
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- ## Intended uses & limitations
 
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- More information needed
 
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- ## Training and evaluation data
 
 
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- More information needed
 
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  ## Training procedure
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  - lr_scheduler_type: linear
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  - num_epochs: 5.0
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- ### Training results
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-
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-
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-
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  ### Framework versions
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  - Transformers 4.27.0.dev0
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  - Pytorch 1.13.1+cu116
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  - Datasets 2.9.0
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- - Tokenizers 0.13.2
 
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  ---
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+ license: apache-2.0
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  tags:
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+ - TDA
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  metrics:
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  - accuracy
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+ - matthews_correlation
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  model-index:
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  - name: roberta-large-cased-en-cola_32_2e-05_lr_0.0001_decay_balanced
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  results: []
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+ datasets:
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+ - shivkumarganesh/CoLA
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+ language:
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+ - en
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  ---
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+ [**Official repository**](https://github.com/upunaprosk/la-tda)
 
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+ # RoBERTa-large-TDA
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+ This model is a version of [roberta-large](https://huggingface.co/roberta-large) fine-tuned on [CoLA](https://nyu-mll.github.io/CoLA/).
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  It achieves the following results on the evaluation set:
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  - Loss: 0.6823
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  - Accuracy: 0.8729
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  - Mcc: 0.6922
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+ ## Features extracted from Transformer
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+ The features extracted from attention maps include the following:
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+ 1. **Topological features** are properties of attention graphs. Features of directed attention graphs include the number of strongly connected components, edges, simple cycles and average vertex degree. The properties of undirected graphs include
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+ the first two Betti numbers: the number of connected components and the number of simple cycles, the matching number and the chordality.
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+ 2. **Features derived from barcodes** include descriptive characteristics of 0/1-dimensional barcodes and reflect the survival (death and birth) of
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+ connected components and edges throughout the filtration.
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+ 3. **Distance-to-pattern** features measure the distance between attention matrices and identity matrices of pre-defined attention patterns, such as attention to the first token [CLS] and to the last
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+ [SEP] of the sequence, attention to previous and
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+ next token and to punctuation marks.
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+ The **computed features and barcodes** can be found in the subdirectories of the repository. *test_sub* features and barcodes were computed on the out of domain test [CoLA dataset](https://www.kaggle.com/c/cola-out-of-domain-open-evaluation/overview).
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+ Refer to notebooks 4* and 5* from the [repository](https://github.com/upunaprosk/la-tda) to construct the classification pipeline with TDA features.
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  ## Training procedure
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  - lr_scheduler_type: linear
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  - num_epochs: 5.0
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  ### Framework versions
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  - Transformers 4.27.0.dev0
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  - Pytorch 1.13.1+cu116
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  - Datasets 2.9.0
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+ - Tokenizers 0.13.2