Training in progress, step 50

README.md

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+---
+library_name: transformers
+base_model: aubmindlab/bert-base-arabertv02-twitter
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+model-index:
+- name: Arabertv2-fold5
+  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. -->
+
+# Arabertv2-fold5
+
+This model is a fine-tuned version of [aubmindlab/bert-base-arabertv02-twitter](https://huggingface.co/aubmindlab/bert-base-arabertv02-twitter) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.6600
+- Accuracy: 0.8155
+- Macro F1: 0.8151
+- Weighted F1: 0.8151
+- F1 Pro: 0.8037
+- F1 Against: 0.8226
+- F1 Neutral: 0.8190
+
+## 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: 16
+- eval_batch_size: 16
+- seed: 42
+- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: linear
+- num_epochs: 10
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch  | Step | Validation Loss | Accuracy | Macro F1 | Weighted F1 | F1 Pro | F1 Against | F1 Neutral |
+|:-------------:|:------:|:----:|:---------------:|:--------:|:--------:|:-----------:|:------:|:----------:|:----------:|
+| 0.9553        | 1.1628 | 50   | 0.7744          | 0.6667   | 0.6675   | 0.6667      | 0.6829 | 0.6387     | 0.6809     |
+| 0.6022        | 2.3256 | 100  | 0.5448          | 0.7619   | 0.7617   | 0.7620      | 0.7748 | 0.7603     | 0.75       |
+| 0.3637        | 3.4884 | 150  | 0.5355          | 0.7976   | 0.7961   | 0.7969      | 0.7961 | 0.8154     | 0.7767     |
+| 0.2713        | 4.6512 | 200  | 0.5420          | 0.8036   | 0.8031   | 0.8035      | 0.8037 | 0.8130     | 0.7925     |
+| 0.1627        | 5.8140 | 250  | 0.5662          | 0.7917   | 0.7914   | 0.7914      | 0.7748 | 0.8033     | 0.7961     |
+| 0.1126        | 6.9767 | 300  | 0.6165          | 0.8095   | 0.8095   | 0.8092      | 0.7963 | 0.8130     | 0.8190     |
+| 0.0886        | 8.1395 | 350  | 0.6597          | 0.8155   | 0.8151   | 0.8151      | 0.8037 | 0.8226     | 0.8190     |
+| 0.0803        | 9.3023 | 400  | 0.6821          | 0.8095   | 0.8089   | 0.8091      | 0.7963 | 0.8226     | 0.8077     |
+
+
+### Framework versions
+
+- Transformers 5.0.0
+- Pytorch 2.10.0+cu128
+- Datasets 4.0.0
+- Tokenizers 0.22.2

config.json

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+{
+  "add_cross_attention": false,
+  "architectures": [
+    "BertForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": null,
+  "classifier_dropout": null,
+  "dtype": "float32",
+  "eos_token_id": null,
+  "finetuning_task": "arabic_sentiment",
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.2,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "positive",
+    "1": "negative",
+    "2": "neutral"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "is_decoder": false,
+  "label2id": {
+    "negative": 1,
+    "neutral": 2,
+    "positive": 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",
+  "tie_word_embeddings": true,
+  "transformers_version": "5.0.0",
+  "type_vocab_size": 2,
+  "use_cache": false,
+  "vocab_size": 30000
+}

model.safetensors

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+size 436358108

tokenizer_config.json

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+{
+  "backend": "tokenizers",
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": false,
+  "is_local": false,
+  "mask_token": "[MASK]",
+  "max_len": 512,
+  "model_max_length": 512,
+  "never_split": [
+    "[بريد]",
+    "[مستخدم]",
+    "[رابط]"
+  ],
+  "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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