End of training

README.md

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+---
+library_name: transformers
+license: mit
+base_model: intfloat/e5-small
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+- precision
+- recall
+- f1
+model-index:
+- name: intfloat-e5-small-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-small-arabic-fp16-allagree
+
+This model is a fine-tuned version of [intfloat/e5-small](https://huggingface.co/intfloat/e5-small) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.5430
+- Accuracy: 0.7845
+- Precision: 0.7994
+- Recall: 0.7845
+- F1: 0.7886
+
+## 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     |
+|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
+| 1.0961        | 0.7463 | 50   | 1.0715          | 0.3853   | 0.7241    | 0.3853 | 0.2241 |
+| 1.0181        | 1.4925 | 100  | 0.9009          | 0.6875   | 0.7524    | 0.6875 | 0.6122 |
+| 0.8328        | 2.2388 | 150  | 0.7633          | 0.7052   | 0.7815    | 0.7052 | 0.6315 |
+| 0.7492        | 2.9851 | 200  | 0.6860          | 0.7295   | 0.6952    | 0.7295 | 0.6858 |
+| 0.6931        | 3.7313 | 250  | 0.6982          | 0.7369   | 0.7275    | 0.7369 | 0.7209 |
+| 0.6623        | 4.4776 | 300  | 0.6326          | 0.7705   | 0.7544    | 0.7705 | 0.7485 |
+| 0.6107        | 5.2239 | 350  | 0.6350          | 0.7556   | 0.7696    | 0.7556 | 0.7530 |
+| 0.5789        | 5.9701 | 400  | 0.5892          | 0.7649   | 0.7924    | 0.7649 | 0.7713 |
+| 0.5593        | 6.7164 | 450  | 0.5449          | 0.7985   | 0.7946    | 0.7985 | 0.7963 |
+| 0.5343        | 7.4627 | 500  | 0.5486          | 0.7845   | 0.8008    | 0.7845 | 0.7897 |
+| 0.5177        | 8.2090 | 550  | 0.5373          | 0.8013   | 0.8038    | 0.8013 | 0.8016 |
+| 0.5201        | 8.9552 | 600  | 0.5370          | 0.7882   | 0.7996    | 0.7882 | 0.7915 |
+| 0.4962        | 9.7015 | 650  | 0.5357          | 0.7882   | 0.7994    | 0.7882 | 0.7917 |
+
+
+### 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-small",
+  "architectures": [
+    "BertForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 384,
+  "id2label": {
+    "0": "negative",
+    "1": "positive",
+    "2": "neutral"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 1536,
+  "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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