End of training

README.md

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+---
+library_name: transformers
+license: mit
+base_model: intfloat/e5-large
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+- precision
+- recall
+- f1
+model-index:
+- name: intfloat-e5-large-arabic-fp16-allagree
+  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-large-arabic-fp16-allagree
+
+This model is a fine-tuned version of [intfloat/e5-large](https://huggingface.co/intfloat/e5-large) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.4301
+- Accuracy: 0.8405
+- Precision: 0.8470
+- Recall: 0.8405
+- F1: 0.8429
+
+## 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     |
+|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
+| 1.0133        | 0.7463 | 50   | 0.6945          | 0.7127   | 0.7471    | 0.7127 | 0.6558 |
+| 0.6316        | 1.4925 | 100  | 0.5125          | 0.8013   | 0.7942    | 0.8013 | 0.7899 |
+| 0.5287        | 2.2388 | 150  | 0.4711          | 0.8041   | 0.8037    | 0.8041 | 0.8034 |
+| 0.4814        | 2.9851 | 200  | 0.4799          | 0.7985   | 0.8288    | 0.7985 | 0.8071 |
+| 0.4304        | 3.7313 | 250  | 0.4373          | 0.8256   | 0.8256    | 0.8256 | 0.8256 |
+| 0.3706        | 4.4776 | 300  | 0.4595          | 0.8293   | 0.8314    | 0.8293 | 0.8283 |
+| 0.3455        | 5.2239 | 350  | 0.4738          | 0.8293   | 0.8455    | 0.8293 | 0.8341 |
+| 0.3053        | 5.9701 | 400  | 0.4301          | 0.8405   | 0.8470    | 0.8405 | 0.8429 |
+| 0.2437        | 6.7164 | 450  | 0.4713          | 0.8358   | 0.8526    | 0.8358 | 0.8409 |
+| 0.223         | 7.4627 | 500  | 0.4894          | 0.8414   | 0.8503    | 0.8414 | 0.8431 |
+| 0.2093        | 8.2090 | 550  | 0.4576          | 0.8554   | 0.8572    | 0.8554 | 0.8562 |
+
+
+### 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,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 1024,
+  "id2label": {
+    "0": "negative",
+    "1": "positive",
+    "2": "neutral"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "label2id": {
+    "negative": 0,
+    "neutral": 2,
+    "positive": 1
+  },
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 24,
+  "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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