End of training

README.md

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+---
+library_name: transformers
+license: mit
+base_model: intfloat/e5-base
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+- precision
+- recall
+- f1
+model-index:
+- name: intfloat-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-e5-base-arabic-fp16-allagree
+
+This model is a fine-tuned version of [intfloat/e5-base](https://huggingface.co/intfloat/e5-base) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.4020
+- Accuracy: 0.8554
+- Precision: 0.8542
+- Recall: 0.8554
+- F1: 0.8547
+
+## 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     |
+|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
+| 1.0217        | 0.7463 | 50   | 0.8492          | 0.6707   | 0.7400    | 0.6707 | 0.5976 |
+| 0.774         | 1.4925 | 100  | 0.6322          | 0.7612   | 0.7671    | 0.7612 | 0.7222 |
+| 0.6181        | 2.2388 | 150  | 0.5103          | 0.8190   | 0.8151    | 0.8190 | 0.8113 |
+| 0.5497        | 2.9851 | 200  | 0.4699          | 0.8218   | 0.8175    | 0.8218 | 0.8182 |
+| 0.4594        | 3.7313 | 250  | 0.4475          | 0.8330   | 0.8330    | 0.8330 | 0.8304 |
+| 0.4155        | 4.4776 | 300  | 0.4371          | 0.8218   | 0.8337    | 0.8218 | 0.8241 |
+| 0.3722        | 5.2239 | 350  | 0.4166          | 0.8461   | 0.8423    | 0.8461 | 0.8427 |
+| 0.3382        | 5.9701 | 400  | 0.4301          | 0.8340   | 0.8483    | 0.8340 | 0.8381 |
+| 0.3087        | 6.7164 | 450  | 0.3906          | 0.8489   | 0.8476    | 0.8489 | 0.8461 |
+| 0.2695        | 7.4627 | 500  | 0.4020          | 0.8601   | 0.8585    | 0.8601 | 0.8588 |
+| 0.2685        | 8.2090 | 550  | 0.4136          | 0.8601   | 0.8601    | 0.8601 | 0.8592 |
+| 0.2451        | 8.9552 | 600  | 0.4020          | 0.8554   | 0.8542    | 0.8554 | 0.8547 |
+
+
+### 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/e5-base",
+  "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": "4.48.2",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

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training_args.bin

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