End of training

README.md

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+---
+library_name: transformers
+license: mit
+base_model: intfloat/e5-base-v2
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+- precision
+- recall
+- f1
+model-index:
+- name: intfloat-e5-base-v2-arabic-fp16
+  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. -->
+
+# intfloat-e5-base-v2-arabic-fp16
+
+This model is a fine-tuned version of [intfloat/e5-base-v2](https://huggingface.co/intfloat/e5-base-v2) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.6634
+- Accuracy: 0.73
+- Precision: 0.7305
+- Recall: 0.73
+- F1: 0.7301
+
+## 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: 64
+- eval_batch_size: 64
+- seed: 42
+- gradient_accumulation_steps: 2
+- total_train_batch_size: 128
+- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_ratio: 0.3
+- num_epochs: 10
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch  | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
+|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
+| 1.0968        | 0.3636 | 50   | 0.9932          | 0.5959   | 0.4804    | 0.5959 | 0.5160 |
+| 0.9488        | 0.7273 | 100  | 0.8474          | 0.6418   | 0.6660    | 0.6418 | 0.5688 |
+| 0.8466        | 1.0873 | 150  | 0.7875          | 0.68     | 0.6629    | 0.68   | 0.6612 |
+| 0.7901        | 1.4509 | 200  | 0.7713          | 0.685    | 0.6766    | 0.685  | 0.6764 |
+| 0.7736        | 1.8145 | 250  | 0.7584          | 0.6895   | 0.6859    | 0.6895 | 0.6860 |
+| 0.7551        | 2.1745 | 300  | 0.7051          | 0.7114   | 0.7047    | 0.7114 | 0.7044 |
+| 0.7253        | 2.5382 | 350  | 0.7309          | 0.6868   | 0.7165    | 0.6868 | 0.6933 |
+| 0.7172        | 2.9018 | 400  | 0.7031          | 0.7177   | 0.7156    | 0.7177 | 0.7165 |
+| 0.6836        | 3.2618 | 450  | 0.6888          | 0.7173   | 0.7228    | 0.7173 | 0.7194 |
+| 0.6733        | 3.6255 | 500  | 0.6981          | 0.7105   | 0.7200    | 0.7105 | 0.7136 |
+| 0.6552        | 3.9891 | 550  | 0.6714          | 0.7341   | 0.7363    | 0.7341 | 0.7231 |
+| 0.6086        | 4.3491 | 600  | 0.6634          | 0.73     | 0.7305    | 0.73   | 0.7301 |
+| 0.6102        | 4.7127 | 650  | 0.6537          | 0.7382   | 0.7437    | 0.7382 | 0.7400 |
+| 0.5929        | 5.0727 | 700  | 0.6812          | 0.7277   | 0.7362    | 0.7277 | 0.7308 |
+| 0.5728        | 5.4364 | 750  | 0.6652          | 0.7286   | 0.7402    | 0.7286 | 0.7293 |
+| 0.5556        | 5.8    | 800  | 0.6694          | 0.7391   | 0.7426    | 0.7391 | 0.7405 |
+
+
+### Framework versions
+
+- Transformers 4.51.1
+- Pytorch 2.6.0+cu124
+- Datasets 3.5.0
+- Tokenizers 0.21.1

config.json

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+{
+  "architectures": [
+    "BertForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "negative",
+    "1": "positive",
+    "2": "neutral"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "negative": 0,
+    "neutral": 2,
+    "positive": 1
+  },
+  "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": "4.51.1",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

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training_args.bin

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