End of training

README.md

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+---
+library_name: transformers
+license: mit
+base_model: intfloat/multilingual-e5-base
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+- precision
+- recall
+- f1
+model-index:
+- name: intfloat-multilingual-e5-base-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-multilingual-e5-base-arabic-fp16-allagree
+
+This model is a fine-tuned version of [intfloat/multilingual-e5-base](https://huggingface.co/intfloat/multilingual-e5-base) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.2194
+- Accuracy: 0.9207
+- Precision: 0.9221
+- Recall: 0.9207
+- F1: 0.9211
+
+## 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     |
+|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
+| 1.0293        | 0.7463 | 50   | 0.8440          | 0.7472   | 0.8005    | 0.7472 | 0.6652 |
+| 0.599         | 1.4925 | 100  | 0.3097          | 0.9021   | 0.9014    | 0.9021 | 0.9017 |
+| 0.2581        | 2.2388 | 150  | 0.2348          | 0.9198   | 0.9207    | 0.9198 | 0.9201 |
+| 0.2087        | 2.9851 | 200  | 0.2194          | 0.9207   | 0.9221    | 0.9207 | 0.9211 |
+| 0.1552        | 3.7313 | 250  | 0.2203          | 0.9300   | 0.9301    | 0.9300 | 0.9299 |
+| 0.0989        | 4.4776 | 300  | 0.2785          | 0.9179   | 0.9181    | 0.9179 | 0.9168 |
+| 0.088         | 5.2239 | 350  | 0.2658          | 0.9263   | 0.9271    | 0.9263 | 0.9266 |
+
+
+### Framework versions
+
+- Transformers 4.48.2
+- Pytorch 2.6.0+cu124
+- Datasets 3.3.1
+- Tokenizers 0.21.0

config.json

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+{
+  "_name_or_path": "intfloat/multilingual-e5-base",
+  "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": 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-05,
+  "max_position_embeddings": 514,
+  "model_type": "xlm-roberta",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "output_past": true,
+  "pad_token_id": 1,
+  "position_embedding_type": "absolute",
+  "problem_type": "single_label_classification",
+  "torch_dtype": "float32",
+  "transformers_version": "4.48.2",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 250002
+}

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training_args.bin

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