Update README.md

README.md

@@ -1,45 +1,13 @@
 ---
 license: apache-2.0
-tags:
-- generated_from_trainer
 metrics:
 - precision
 - recall
 - f1
 - accuracy
-widget:
-- text: The process starts when the customer enters the shop. The customer then takes
-    the product from the shelf. The customer then pays for the product and leaves
-    the store.
-  example_title: Example 1
-- text: The process begins when the HR department hires the new employee. Next, the
-    new employee completes necessary paperwork and provides documentation to the HR
-    department. After the initial task, the HR department performs a  decision to
-    determine the employee's role and department assignment. The employee is trained
-    by the Sales department. After the training, the Sales department assigns the
-    employee a sales quota and performance goals. Finally, the process ends with an
-    'End' event, when the employee begins their role in the Sales department.
-  example_title: Example 2
-- text: A customer places an order for a product on the company's website. Next, the
-    customer service department checks the availability of the product and confirms
-    the order with the customer. After the initial task, the warehouse  processes
-    the order. If the order is eligible for same-day shipping, the warehouse staff
-    picks and packs the order, and it is sent to the shipping department. After the
-    order is packed, the shipping department delivers the order to the customer. Finally,
-    the process ends with an 'End' event, when the customer receives their order.
-  example_title: Example 3
-base_model: bert-base-cased
-model-index:
-- name: bert-finetuned-v4
-  results: []
----
-
-<!-- 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. -->
 
-# bpmn-information-extraction
 
-This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on a dataset containing 90 textual process descriptions.
+# BPMN element detection
 
 The dataset contains 5 target labels:
 
@@ -58,7 +26,7 @@ It achieves the following results on the evaluation set:
 
 ## Model description
 
-More information needed
+This project aims to detect Business Process Model and Notation (BPMN) elements from hand-drawn diagrams using a machine learning model. The model is trained to recognize various BPMN elements such as tasks, events, gateways, and connectors from images of hand-drawn diagrams.
 
 ## Intended uses & limitations
 
@@ -73,13 +41,13 @@ More information needed
 ### Training hyperparameters
 
 The following hyperparameters were used during training:
-- learning_rate: 2e-05
-- train_batch_size: 8
-- eval_batch_size: 8
+- learning_rate: 
+- train_batch_size: 
+- eval_batch_size: 
 - seed: 42
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
-- num_epochs: 15
+- num_epochs: 
 
 ### Training results
 
@@ -99,12 +67,4 @@ The following hyperparameters were used during training:
 | 0.059         | 12.0  | 120  | 0.2886          | 0.8564    | 0.9301 | 0.8918 | 0.9285   |
 | 0.0528        | 13.0  | 130  | 0.2838          | 0.8564    | 0.9301 | 0.8918 | 0.9305   |
 | 0.0488        | 14.0  | 140  | 0.2881          | 0.8515    | 0.9247 | 0.8866 | 0.9305   |
-| 0.049         | 15.0  | 150  | 0.2909          | 0.8557    | 0.9247 | 0.8889 | 0.9285   |
-
-
-### Framework versions
-
-- Transformers 4.25.1
-- Pytorch 1.13.0+cu116
-- Datasets 2.8.0
-- Tokenizers 0.13.2
+| 0.049         | 15.0  | 150  | 0.2909          | 0.8557    | 0.9247 | 0.8889 | 0.9285   |