End of training

README.md

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+---
+library_name: transformers
+license: mit
+base_model: intfloat/multilingual-e5-large
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+- precision
+- recall
+- f1
+model-index:
+- name: intfloat-multilingual-e5-large-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-multilingual-e5-large-arabic-fp16
+
+This model is a fine-tuned version of [intfloat/multilingual-e5-large](https://huggingface.co/intfloat/multilingual-e5-large) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.4707
+- Accuracy: 0.8177
+- Precision: 0.8189
+- Recall: 0.8177
+- F1: 0.8176
+
+## 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     |
+|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
+| 1.0811        | 0.3636 | 50   | 0.9500          | 0.57     | 0.7318    | 0.57   | 0.4829 |
+| 0.8355        | 0.7273 | 100  | 0.6392          | 0.7605   | 0.7622    | 0.7605 | 0.7510 |
+| 0.5786        | 1.0873 | 150  | 0.5154          | 0.7986   | 0.7983    | 0.7986 | 0.7981 |
+| 0.508         | 1.4509 | 200  | 0.4863          | 0.8155   | 0.8144    | 0.8155 | 0.8139 |
+| 0.4931        | 1.8145 | 250  | 0.4732          | 0.8114   | 0.8102    | 0.8114 | 0.8103 |
+| 0.4708        | 2.1745 | 300  | 0.4988          | 0.8164   | 0.8184    | 0.8164 | 0.8163 |
+| 0.4146        | 2.5382 | 350  | 0.5011          | 0.8073   | 0.8110    | 0.8073 | 0.8079 |
+| 0.4436        | 2.9018 | 400  | 0.4707          | 0.8177   | 0.8189    | 0.8177 | 0.8176 |
+| 0.3741        | 3.2618 | 450  | 0.5440          | 0.8      | 0.8042    | 0.8    | 0.8013 |
+| 0.3358        | 3.6255 | 500  | 0.5422          | 0.7982   | 0.8063    | 0.7982 | 0.7994 |
+| 0.3497        | 3.9891 | 550  | 0.5210          | 0.8105   | 0.8147    | 0.8105 | 0.8102 |
+
+
+### 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": [
+    "XLMRobertaForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "classifier_dropout": null,
+  "eos_token_id": 2,
+  "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-05,
+  "max_position_embeddings": 514,
+  "model_type": "xlm-roberta",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 24,
+  "output_past": true,
+  "pad_token_id": 1,
+  "position_embedding_type": "absolute",
+  "problem_type": "single_label_classification",
+  "torch_dtype": "float32",
+  "transformers_version": "4.51.1",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 250002
+}

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training_args.bin

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