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
+  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
+
+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: 0.4961
+- Accuracy: 0.7986
+- Precision: 0.7991
+- Recall: 0.7986
+- F1: 0.7988
+
+## 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     |
+|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
+| 1.0686        | 0.3636 | 50   | 1.0146          | 0.5495   | 0.7252    | 0.5495 | 0.4582 |
+| 0.9589        | 0.7273 | 100  | 0.8046          | 0.6777   | 0.7234    | 0.6777 | 0.6081 |
+| 0.7431        | 1.0873 | 150  | 0.6238          | 0.7595   | 0.7565    | 0.7595 | 0.7530 |
+| 0.6066        | 1.4509 | 200  | 0.5485          | 0.7945   | 0.7947    | 0.7945 | 0.7906 |
+| 0.5558        | 1.8145 | 250  | 0.5530          | 0.7827   | 0.7860    | 0.7827 | 0.7837 |
+| 0.5343        | 2.1745 | 300  | 0.5430          | 0.7973   | 0.8009    | 0.7973 | 0.7983 |
+| 0.4965        | 2.5382 | 350  | 0.5178          | 0.7986   | 0.7993    | 0.7986 | 0.7988 |
+| 0.5017        | 2.9018 | 400  | 0.4961          | 0.7986   | 0.7991    | 0.7986 | 0.7988 |
+| 0.4525        | 3.2618 | 450  | 0.5441          | 0.7932   | 0.7991    | 0.7932 | 0.7950 |
+| 0.4194        | 3.6255 | 500  | 0.5147          | 0.8027   | 0.8051    | 0.8027 | 0.8027 |
+| 0.4353        | 3.9891 | 550  | 0.4918          | 0.8118   | 0.8109    | 0.8118 | 0.8110 |
+| 0.3635        | 4.3491 | 600  | 0.5659          | 0.7977   | 0.8058    | 0.7977 | 0.7980 |
+| 0.3529        | 4.7127 | 650  | 0.5493          | 0.8023   | 0.8066    | 0.8023 | 0.8029 |
+| 0.3574        | 5.0727 | 700  | 0.5438          | 0.8023   | 0.8043    | 0.8023 | 0.8031 |
+
+
+### 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": 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": "4.51.1",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 250002
+}

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training_args.bin

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