Finished training

README.md

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+---
+license: mit
+base_model: google-bert/bert-base-german-cased
+tags:
+- generated_from_trainer
+model-index:
+- name: bert-mapa-german
+ 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. -->
+
+# bert-mapa-german
+
+This model is a fine-tuned version of [google-bert/bert-base-german-cased](https://huggingface.co/google-bert/bert-base-german-cased) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0325
+- Address: {'precision': 0.5882352941176471, 'recall': 0.6666666666666666, 'f1': 0.625, 'number': 15}
+- Age: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3}
+- Amount: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1}
+- Date: {'precision': 0.9454545454545454, 'recall': 0.9454545454545454, 'f1': 0.9454545454545454, 'number': 55}
+- Name: {'precision': 0.7, 'recall': 0.9545454545454546, 'f1': 0.8076923076923077, 'number': 22}
+- Organisation: {'precision': 0.5405405405405406, 'recall': 0.6451612903225806, 'f1': 0.588235294117647, 'number': 31}
+- Person: {'precision': 0.5384615384615384, 'recall': 0.5, 'f1': 0.5185185185185186, 'number': 14}
+- Role: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1}
+- Overall Precision: 0.7255
+- Overall Recall: 0.7817
+- Overall F1: 0.7525
+- Overall Accuracy: 0.9912
+
+## 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: 4
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Address | Amount | Date | Marital status | Name | Organisation | Person | Profession | Role | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:------------------------------------------------------------------------------------------:|:---------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------:|:---------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| No log | 1.0 | 218 | 0.0607 | {'precision': 0.5882352941176471, 'recall': 0.6666666666666666, 'f1': 0.625, 'number': 15} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 3} | {'precision': 0.851063829787234, 'recall': 0.9090909090909091, 'f1': 0.8791208791208791, 'number': 44} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 0.76, 'recall': 0.9047619047619048, 'f1': 0.8260869565217391, 'number': 21} | {'precision': 0.4915254237288136, 'recall': 0.725, 'f1': 0.5858585858585859, 'number': 40} | {'precision': 0.5, 'recall': 0.6153846153846154, 'f1': 0.5517241379310345, 'number': 13} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1} | 0.6527 | 0.7786 | 0.7101 | 0.9859 |
+| No log | 2.0 | 436 | 0.0479 | {'precision': 0.65, 'recall': 0.8666666666666667, 'f1': 0.7428571428571429, 'number': 15} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 3} | {'precision': 0.8958333333333334, 'recall': 0.9772727272727273, 'f1': 0.9347826086956522, 'number': 44} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 0.6774193548387096, 'recall': 1.0, 'f1': 0.8076923076923077, 'number': 21} | {'precision': 0.5897435897435898, 'recall': 0.575, 'f1': 0.5822784810126582, 'number': 40} | {'precision': 0.7857142857142857, 'recall': 0.8461538461538461, 'f1': 0.8148148148148148, 'number': 13} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1} | 0.7355 | 0.8143 | 0.7729 | 0.9896 |
+| 0.116 | 3.0 | 654 | 0.0414 | {'precision': 0.65, 'recall': 0.8666666666666667, 'f1': 0.7428571428571429, 'number': 15} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 3} | {'precision': 0.8958333333333334, 'recall': 0.9772727272727273, 'f1': 0.9347826086956522, 'number': 44} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 0.7407407407407407, 'recall': 0.9523809523809523, 'f1': 0.8333333333333334, 'number': 21} | {'precision': 0.725, 'recall': 0.725, 'f1': 0.7250000000000001, 'number': 40} | {'precision': 0.6666666666666666, 'recall': 0.7692307692307693, 'f1': 0.7142857142857142, 'number': 13} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1} | 0.7712 | 0.8429 | 0.8055 | 0.9908 |
+| 0.116 | 4.0 | 872 | 0.0421 | {'precision': 0.65, 'recall': 0.8666666666666667, 'f1': 0.7428571428571429, 'number': 15} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 3} | {'precision': 0.8958333333333334, 'recall': 0.9772727272727273, 'f1': 0.9347826086956522, 'number': 44} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 0.8, 'recall': 0.9523809523809523, 'f1': 0.8695652173913043, 'number': 21} | {'precision': 0.6818181818181818, 'recall': 0.75, 'f1': 0.7142857142857143, 'number': 40} | {'precision': 0.8571428571428571, 'recall': 0.9230769230769231, 'f1': 0.888888888888889, 'number': 13} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1} | 0.7857 | 0.8643 | 0.8231 | 0.9917 |
+
+
+### Framework versions
+
+- Transformers 4.40.0
+- Pytorch 2.1.0+cu121
+- Datasets 2.19.0
+- Tokenizers 0.19.1

config.json

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+{
+ "_name_or_path": "google-bert/bert-base-german-cased",
+ "architectures": [
+ "BertForTokenClassification"
+ ],
+ "attention_probs_dropout_prob": 0.1,
+ "classifier_dropout": null,
+ "hidden_act": "gelu",
+ "hidden_dropout_prob": 0.1,
+ "hidden_size": 768,
+ "id2label": {
+ "0": "O",
+ "1": "B-ADDRESS",
+ "2": "I-ADDRESS",
+ "3": "B-DATE",
+ "4": "I-DATE",
+ "5": "B-ORGANISATION",
+ "6": "I-ORGANISATION",
+ "7": "B-NAME",
+ "8": "I-NAME",
+ "9": "B-PERSON",
+ "10": "I-PERSON",
+ "11": "B-ROLE",
+ "12": "I-ROLE",
+ "13": "B-PROFESSION",
+ "14": "I-PROFESSION",
+ "15": "B-AMOUNT",
+ "16": "I-AMOUNT",
+ "17": "B-AGE",
+ "18": "I-AGE",
+ "19": "B-MARITAL STATUS",
+ "20": "I-MARITAL STATUS",
+ "21": "B-ETHNIC CATEGORY",
+ "22": "I-ETHNIC CATEGORY",
+ "23": "B-NATIONALITY",
+ "24": "I-NATIONALITY"
+ },
+ "initializer_range": 0.02,
+ "intermediate_size": 3072,
+ "label2id": {
+ "B-ADDRESS": 1,
+ "B-AGE": 17,
+ "B-AMOUNT": 15,
+ "B-DATE": 3,
+ "B-ETHNIC CATEGORY": 21,
+ "B-MARITAL STATUS": 19,
+ "B-NAME": 7,
+ "B-NATIONALITY": 23,
+ "B-ORGANISATION": 5,
+ "B-PERSON": 9,
+ "B-PROFESSION": 13,
+ "B-ROLE": 11,
+ "I-ADDRESS": 2,
+ "I-AGE": 18,
+ "I-AMOUNT": 16,
+ "I-DATE": 4,
+ "I-ETHNIC CATEGORY": 22,
+ "I-MARITAL STATUS": 20,
+ "I-NAME": 8,
+ "I-NATIONALITY": 24,
+ "I-ORGANISATION": 6,
+ "I-PERSON": 10,
+ "I-PROFESSION": 14,
+ "I-ROLE": 12,
+ "O": 0
+ },
+ "layer_norm_eps": 1e-12,
+ "max_position_embeddings": 512,
+ "model_type": "bert",
+ "num_attention_heads": 12,
+ "num_hidden_layers": 12,
+ "pad_token_id": 0,
+ "position_embedding_type": "absolute",
+ "torch_dtype": "float32",
+ "transformers_version": "4.40.0",
+ "type_vocab_size": 2,
+ "use_cache": true,
+ "vocab_size": 30000
+}

model.safetensors

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+oid sha256:9057eb525021c8fbd2d8c9e244f581bc9edf48d5bed2717d07dc6962020d6cd7
+size 434063252

special_tokens_map.json

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+{
+ "cls_token": "[CLS]",
+ "mask_token": "[MASK]",
+ "pad_token": "[PAD]",
+ "sep_token": "[SEP]",
+ "unk_token": "[UNK]"
+}

tokenizer_config.json

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+{
+ "added_tokens_decoder": {
+ "0": {
+ "content": "[PAD]",
+ "lstrip": false,
+ "normalized": false,
+ "rstrip": false,
+ "single_word": false,
+ "special": true
+ },
+ "2": {
+ "content": "[UNK]",
+ "lstrip": false,
+ "normalized": false,
+ "rstrip": false,
+ "single_word": false,
+ "special": true
+ },
+ "3": {
+ "content": "[CLS]",
+ "lstrip": false,
+ "normalized": false,
+ "rstrip": false,
+ "single_word": false,
+ "special": true
+ },
+ "4": {
+ "content": "[SEP]",
+ "lstrip": false,
+ "normalized": false,
+ "rstrip": false,
+ "single_word": false,
+ "special": true
+ },
+ "5": {
+ "content": "[MASK]",
+ "lstrip": false,
+ "normalized": false,
+ "rstrip": false,
+ "single_word": false,
+ "special": true
+ }
+ },
+ "clean_up_tokenization_spaces": true,
+ "cls_token": "[CLS]",
+ "do_lower_case": false,
+ "mask_token": "[MASK]",
+ "model_max_length": 512,
+ "pad_token": "[PAD]",
+ "sep_token": "[SEP]",
+ "strip_accents": null,
+ "tokenize_chinese_chars": true,
+ "tokenizer_class": "BertTokenizer",
+ "unk_token": "[UNK]"
+}

training_args.bin

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+oid sha256:ff9df7507025cdc300d4ca63aef319fb27233fa244fc064689e7f53443c8ec24
+size 4984