Update README.md

README.md

@@ -1,36 +1,45 @@
 ---
-license: mit
+license: apache-2.0
 tags:
-- generated_from_trainer
+- TDA
 metrics:
 - accuracy
+- matthews_correlation
 model-index:
 - name: bert-base-italian-cased-ita-cola_64_3e-05_lr_0.01_decay_balanced
   results: []
+datasets:
+- gsarti/itacola
+language:
+- it
 ---
 
-<!-- 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-italian-cased-ita-cola_64_3e-05_lr_0.01_decay_balanced
+# ItaBERT-TDA
 
-This model is a fine-tuned version of [dbmdz/bert-base-italian-cased](https://huggingface.co/dbmdz/bert-base-italian-cased) on an unknown dataset.
+This model is a version of [dbmdz/bert-base-italian-cased](https://huggingface.co/dbmdz/bert-base-italian-cased) fine-tuned on [ItaCoLA](https://github.com/dhfbk/itacola-dataset).
 It achieves the following results on the evaluation set:
 - Loss: 0.5797
 - Accuracy: 0.8668
 - Mcc: 0.4265
 
-## 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. 
+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
 
@@ -45,13 +54,9 @@ The following hyperparameters were used during training:
 - lr_scheduler_type: linear
 - num_epochs: 3.0
 
-### Training results
-
-
-
 ### Framework versions
 
 - Transformers 4.23.0.dev0
 - Pytorch 1.12.1+cu113
 - Datasets 2.5.1
-- Tokenizers 0.13.0
+- Tokenizers 0.13.0