End of training

README.md

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+---
+library_name: transformers
+license: mit
+base_model: intfloat/e5-large
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+- precision
+- recall
+- f1
+model-index:
+- name: intfloat-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-e5-large-arabic-fp16
+
+This model is a fine-tuned version of [intfloat/e5-large](https://huggingface.co/intfloat/e5-large) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.6571
+- Accuracy: 0.7295
+- Precision: 0.7252
+- Recall: 0.7295
+- F1: 0.7229
+
+## 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     |
+|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
+| 1.1453        | 0.3636 | 50   | 0.9382          | 0.5823   | 0.4522    | 0.5823 | 0.5088 |
+| 0.9116        | 0.7273 | 100  | 0.8151          | 0.6568   | 0.6543    | 0.6568 | 0.6162 |
+| 0.8321        | 1.0873 | 150  | 0.8027          | 0.6645   | 0.6610    | 0.6645 | 0.6379 |
+| 0.8035        | 1.4509 | 200  | 0.7924          | 0.6777   | 0.6807    | 0.6777 | 0.6628 |
+| 0.7746        | 1.8145 | 250  | 0.9196          | 0.6141   | 0.6605    | 0.6141 | 0.6040 |
+| 0.7751        | 2.1745 | 300  | 0.7843          | 0.6677   | 0.6741    | 0.6677 | 0.6650 |
+| 0.753         | 2.5382 | 350  | 0.7799          | 0.6568   | 0.6968    | 0.6568 | 0.6672 |
+| 0.731         | 2.9018 | 400  | 0.7178          | 0.7123   | 0.7160    | 0.7123 | 0.6950 |
+| 0.7133        | 3.2618 | 450  | 0.6932          | 0.71     | 0.7151    | 0.71   | 0.7117 |
+| 0.7057        | 3.6255 | 500  | 0.7281          | 0.6986   | 0.7044    | 0.6986 | 0.6988 |
+| 0.6831        | 3.9891 | 550  | 0.6745          | 0.7309   | 0.7296    | 0.7309 | 0.7195 |
+| 0.6486        | 4.3491 | 600  | 0.6571          | 0.7295   | 0.7252    | 0.7295 | 0.7229 |
+| 0.6378        | 4.7127 | 650  | 0.6701          | 0.7232   | 0.7217    | 0.7232 | 0.7223 |
+| 0.6281        | 5.0727 | 700  | 0.6627          | 0.7386   | 0.7350    | 0.7386 | 0.7360 |
+| 0.5938        | 5.4364 | 750  | 0.6814          | 0.7155   | 0.7229    | 0.7155 | 0.7181 |
+
+
+### 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,
+  "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-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 24,
+  "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
+}

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training_args.bin

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