End of training

README.md

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+---
+license: apache-2.0
+base_model: indolem/indobertweet-base-uncased
+tags:
+- generated_from_trainer
+model-index:
+- name: classification-hate-speech-DE-14-twit
+  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. -->
+
+# classification-hate-speech-DE-14-twit
+
+This model is a fine-tuned version of [indolem/indobertweet-base-uncased](https://huggingface.co/indolem/indobertweet-base-uncased) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 2.7131
+- F1 macro: 0.4211
+- Weighted: 0.6318
+- Balanced accuracy: 0.5466
+
+## 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: 5e-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: 14
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | F1 macro | Weighted | Balanced accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:-----------------:|
+| 1.2526        | 1.0   | 152  | 1.0691          | 0.4123   | 0.6723   | 0.4918            |
+| 0.8027        | 2.0   | 304  | 1.0656          | 0.4134   | 0.6745   | 0.4853            |
+| 0.2427        | 3.0   | 456  | 1.6157          | 0.4341   | 0.6215   | 0.5589            |
+| 0.0699        | 4.0   | 608  | 1.8798          | 0.3919   | 0.6305   | 0.5128            |
+| 0.0721        | 5.0   | 760  | 2.5604          | 0.3917   | 0.5534   | 0.5298            |
+| 0.0514        | 6.0   | 912  | 2.4451          | 0.3949   | 0.6116   | 0.5375            |
+| 0.002         | 7.0   | 1064 | 2.6198          | 0.4128   | 0.6078   | 0.5445            |
+| 0.0161        | 8.0   | 1216 | 2.6067          | 0.4185   | 0.6181   | 0.5441            |
+| 0.0007        | 9.0   | 1368 | 2.5439          | 0.4209   | 0.6326   | 0.5436            |
+| 0.0006        | 10.0  | 1520 | 2.6588          | 0.4199   | 0.6296   | 0.5476            |
+| 0.0005        | 11.0  | 1672 | 2.6799          | 0.4210   | 0.6321   | 0.5481            |
+| 0.0004        | 12.0  | 1824 | 2.6578          | 0.4223   | 0.6358   | 0.5458            |
+| 0.0004        | 13.0  | 1976 | 2.6912          | 0.4237   | 0.6369   | 0.5476            |
+| 0.0005        | 14.0  | 2128 | 2.7131          | 0.4211   | 0.6318   | 0.5466            |
+
+
+### Framework versions
+
+- Transformers 4.41.2
+- Pytorch 2.3.0+cu121
+- Datasets 2.20.0
+- Tokenizers 0.19.1

config.json

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+{
+  "_name_or_path": "indolem/indobertweet-base-uncased",
+  "architectures": [
+    "BertForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "classifier_dropout": null,
+  "eos_token_ids": 0,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "LABEL_0",
+    "1": "LABEL_1",
+    "2": "LABEL_2",
+    "3": "LABEL_3",
+    "4": "LABEL_4",
+    "5": "LABEL_5",
+    "6": "LABEL_6",
+    "7": "LABEL_7"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "LABEL_0": 0,
+    "LABEL_1": 1,
+    "LABEL_2": 2,
+    "LABEL_3": 3,
+    "LABEL_4": 4,
+    "LABEL_5": 5,
+    "LABEL_6": 6,
+    "LABEL_7": 7
+  },
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "output_past": true,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "problem_type": "single_label_classification",
+  "torch_dtype": "float32",
+  "transformers_version": "4.41.2",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 31923
+}

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