Model save

README.md

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+---
+license: mit
+base_model: google-bert/bert-base-german-cased
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+model-index:
+- name: BERT_Reciever_6
+  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_Reciever_6
+
+This model is a fine-tuned version of [google-bert/bert-base-german-cased](https://huggingface.co/google-bert/bert-base-german-cased) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.1356
+- Accuracy: 0.9561
+
+## 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: 0.0001
+- train_batch_size: 160
+- eval_batch_size: 160
+- seed: 2805
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 10.0
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|
+| No log        | 1.0   | 31   | 0.3889          | 0.9196   |
+| No log        | 2.0   | 62   | 0.2137          | 0.9342   |
+| No log        | 3.0   | 93   | 0.1763          | 0.9433   |
+| No log        | 4.0   | 124  | 0.1660          | 0.9433   |
+| No log        | 5.0   | 155  | 0.1592          | 0.9415   |
+| No log        | 6.0   | 186  | 0.1522          | 0.9470   |
+| No log        | 7.0   | 217  | 0.1454          | 0.9525   |
+| No log        | 8.0   | 248  | 0.1448          | 0.9506   |
+| No log        | 9.0   | 279  | 0.1388          | 0.9525   |
+| No log        | 10.0  | 310  | 0.1356          | 0.9561   |
+
+
+### Framework versions
+
+- Transformers 4.44.0.dev0
+- Pytorch 2.3.1+cu121
+- Datasets 2.20.0
+- Tokenizers 0.19.1

config.json

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+{
+  "_name_or_path": "google-bert/bert-base-german-cased",
+  "architectures": [
+    "BertForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "finetuning_task": "text-classification",
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
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+  "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",
+  "problem_type": "single_label_classification",
+  "torch_dtype": "float32",
+  "transformers_version": "4.44.0.dev0",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30000
+}

model.safetensors

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runs/Aug06_13-28-33_10db6dd1f421/events.out.tfevents.1722950924.10db6dd1f421.959.0

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special_tokens_map.json

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

tokenizer_config.json

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+  "added_tokens_decoder": {
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+  "do_lower_case": false,
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+  "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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