Update README.md

README.md

@@ -1,43 +1,52 @@
 ---
 license: apache-2.0
 tags:
-- generated_from_trainer
+- TDA
 metrics:
 - accuracy
+- matthews_correlation
 model-index:
 - name: bert-base-cased-ru-cola_32_3e-05_lr_0.1_decay_balanced
   results: []
+datasets:
+- RussianNLP/rucola
+language:
+- ru
 ---
 
-<!-- This model card has been generated automatically according to the information the Trainer had access to. You
-should probably proofread and complete it, then remove this comment. -->
+[**Official repository**](https://github.com/upunaprosk/la-tda)
 
-# bert-base-cased-ru-cola_32_3e-05_lr_0.1_decay_balanced
+# RuRoBERTa-large-TDA
 
-This model is a fine-tuned version of [sberbank-ai/ruBert-base](https://huggingface.co/sberbank-ai/ruBert-base) on an unknown dataset.
+This model is a version of [sberbank-ai/ruBert-base](https://huggingface.co/sberbank-ai/ruBert-base) fine-tuned on [RuCoLA](https://huggingface.co/datasets/RussianNLP/rucola).
 It achieves the following results on the evaluation set:
 - Loss: 1.5267
 - Accuracy: 0.8026
 - Mcc: 0.4195
 
-## Model description
+## Features extracted from Transformer
 
-More information needed
+The features extracted from attention maps include the following:
 
-## Intended uses & limitations
+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
+the first two Betti numbers: the number of connected components and the number of simple cycles, the matching number and the chordality.
 
-More information needed
+2. **Features derived from barcodes** include descriptive characteristics of 0/1-dimensional barcodes and reflect the survival (death and birth) of
+connected components and edges throughout the filtration.
 
-## Training and evaluation data
+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
+[SEP] of the sequence, attention to previous and
+next token and to punctuation marks.
 
-More information needed
+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 RuCoLA dataset.
+Refer to notebooks 4* and 5* from the [repository](https://github.com/upunaprosk/la-tda) to construct the classification pipeline with TDA features.
 
 ## Training procedure
 
 ### Training hyperparameters
 
 The following hyperparameters were used during training:
-- learning_rate: 3e-05
+- learning_rate: 1e-05
 - train_batch_size: 32
 - eval_batch_size: 8
 - seed: 42
@@ -45,13 +54,9 @@ The following hyperparameters were used during training:
 - lr_scheduler_type: linear
 - num_epochs: 5.0
 
-### Training results
-
-
-
 ### Framework versions
 
 - Transformers 4.27.0.dev0
 - Pytorch 1.13.1+cu116
 - Datasets 2.9.0
-- Tokenizers 0.13.2
+- Tokenizers 0.13.2