Update README.md

README.md

@@ -1,13 +1,12 @@
 ---
-license: mpl-2.0
-language:
-- en
+license: apache-2.0
+tags:
+- generated_from_trainer
 metrics:
+- precision
+- recall
 - f1
 - accuracy
-- recall
-- precision
-pipeline_tag: image-to-text
 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
@@ -29,4 +28,83 @@ widget:
     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.
+
+The dataset contains 5 target labels:
+
+* `AGENT`
+* `TASK`
+* `TASK_INFO`
+* `PROCESS_INFO`
+* `CONDITION`
+
+It achieves the following results on the evaluation set:
+- Loss: 0.2909
+- Precision: 0.8557
+- Recall: 0.9247
+- F1: 0.8889
+- Accuracy: 0.9285
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 2e-05
+- train_batch_size: 8
+- eval_batch_size: 8
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 15
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 2.0586        | 1.0   | 10   | 1.5601          | 0.1278    | 0.1559 | 0.1404 | 0.4750   |
+| 1.3702        | 2.0   | 20   | 1.0113          | 0.3947    | 0.5645 | 0.4646 | 0.7150   |
+| 0.8872        | 3.0   | 30   | 0.6645          | 0.5224    | 0.6882 | 0.5940 | 0.8051   |
+| 0.5341        | 4.0   | 40   | 0.4741          | 0.6754    | 0.8280 | 0.7440 | 0.8541   |
+| 0.3221        | 5.0   | 50   | 0.3831          | 0.7523    | 0.8817 | 0.8119 | 0.8883   |
+| 0.2168        | 6.0   | 60   | 0.3297          | 0.7731    | 0.8978 | 0.8308 | 0.9079   |
+| 0.1565        | 7.0   | 70   | 0.2998          | 0.8195    | 0.9032 | 0.8593 | 0.9128   |
+| 0.1227        | 8.0   | 80   | 0.3227          | 0.8038    | 0.9032 | 0.8506 | 0.9099   |
+| 0.0957        | 9.0   | 90   | 0.2840          | 0.8431    | 0.9247 | 0.8821 | 0.9216   |
+| 0.077         | 10.0  | 100  | 0.2914          | 0.8252    | 0.9140 | 0.8673 | 0.9216   |
+| 0.0691        | 11.0  | 110  | 0.2850          | 0.8431    | 0.9247 | 0.8821 | 0.9285   |
+| 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