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
+  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
+
+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: 0.7482
+- Accuracy: 0.6909
+- Precision: 0.6879
+- Recall: 0.6909
+- F1: 0.6881
+
+## 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     |
+|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
+| 1.0832        | 0.3636 | 50   | 1.0122          | 0.49     | 0.6672    | 0.49   | 0.3741 |
+| 0.9697        | 0.7273 | 100  | 0.8935          | 0.6073   | 0.5817    | 0.6073 | 0.5493 |
+| 0.8744        | 1.0873 | 150  | 0.8016          | 0.6636   | 0.6552    | 0.6636 | 0.6272 |
+| 0.8115        | 1.4509 | 200  | 0.7482          | 0.6909   | 0.6879    | 0.6909 | 0.6881 |
+| 0.7757        | 1.8145 | 250  | 0.8217          | 0.6482   | 0.6747    | 0.6482 | 0.6500 |
+| 0.7566        | 2.1745 | 300  | 0.7877          | 0.6518   | 0.6874    | 0.6518 | 0.6610 |
+| 0.7325        | 2.5382 | 350  | 0.8127          | 0.6436   | 0.6968    | 0.6436 | 0.6553 |
+
+
+### 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,
+  "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": "4.51.1",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

model.safetensors

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runs/Apr16_16-41-05_c1766b0a1233/events.out.tfevents.1744821677.c1766b0a1233.2393.6

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training_args.bin

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