Upload folder using huggingface_hub
Browse files- epoch4/error.log +0 -0
- epoch4/logs.txt +474 -0
- epoch4/model/1_Pooling/config.json +10 -0
- epoch4/model/README.md +662 -0
- epoch4/model/config.json +28 -0
- epoch4/model/config_sentence_transformers.json +14 -0
- epoch4/model/model.safetensors +3 -0
- epoch4/model/modules.json +14 -0
- epoch4/model/sentence_bert_config.json +4 -0
- epoch4/model/tokenizer.json +0 -0
- epoch4/model/tokenizer_config.json +21 -0
- epoch4/output.log +113 -0
epoch4/error.log
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epoch4/logs.txt
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| 1 |
+
2026-03-02 17:54:30 - Load pretrained SentenceTransformer: bert-base-arabertv02
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| 2 |
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2026-03-02 17:54:41 - '[Errno -2] Name or service not known' thrown while requesting HEAD https://huggingface.co/bert-base-arabertv02/resolve/main/./modules.json
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| 3 |
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2026-03-02 17:54:41 - Retrying in 1s [Retry 1/5].
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| 4 |
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2026-03-02 17:54:42 - No sentence-transformers model found with name bert-base-arabertv02. Creating a new one with mean pooling.
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| 5 |
+
2026-03-02 17:56:12 - Use pytorch device_name: cuda:0
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| 6 |
+
2026-03-02 17:56:12 - Load pretrained SentenceTransformer: /home/skiredj.abderrahman/khalil/sbert_training/output/arabert_20260224_1730/checkpoint-61786
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| 7 |
+
{'loss': '0.4484', 'grad_norm': '4.876', 'learning_rate': '1.111e-05', 'epoch': '2'}
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| 8 |
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{'loss': '0.4408', 'grad_norm': '5.164', 'learning_rate': '1.107e-05', 'epoch': '2.007'}
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| 9 |
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{'loss': '0.4188', 'grad_norm': '3.438', 'learning_rate': '1.104e-05', 'epoch': '2.013'}
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| 10 |
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{'loss': '0.4396', 'grad_norm': '7.06', 'learning_rate': '1.1e-05', 'epoch': '2.02'}
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| 11 |
+
{'loss': '0.4423', 'grad_norm': '4.074', 'learning_rate': '1.096e-05', 'epoch': '2.026'}
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| 12 |
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{'loss': '0.4464', 'grad_norm': '6.44', 'learning_rate': '1.093e-05', 'epoch': '2.033'}
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| 13 |
+
{'loss': '0.4318', 'grad_norm': '3.624', 'learning_rate': '1.089e-05', 'epoch': '2.039'}
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| 14 |
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{'loss': '0.4413', 'grad_norm': '3.332', 'learning_rate': '1.086e-05', 'epoch': '2.046'}
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| 15 |
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{'loss': '0.4612', 'grad_norm': '4.731', 'learning_rate': '1.082e-05', 'epoch': '2.052'}
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| 16 |
+
{'loss': '0.4406', 'grad_norm': '5.413', 'learning_rate': '1.079e-05', 'epoch': '2.059'}
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| 17 |
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{'loss': '0.4376', 'grad_norm': '3.114', 'learning_rate': '1.075e-05', 'epoch': '2.065'}
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| 18 |
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{'loss': '0.445', 'grad_norm': '4.638', 'learning_rate': '1.071e-05', 'epoch': '2.072'}
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| 19 |
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{'loss': '0.4373', 'grad_norm': '3.384', 'learning_rate': '1.068e-05', 'epoch': '2.078'}
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| 20 |
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{'loss': '0.4227', 'grad_norm': '5.522', 'learning_rate': '1.064e-05', 'epoch': '2.085'}
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| 21 |
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{'loss': '0.4499', 'grad_norm': '2.805', 'learning_rate': '1.061e-05', 'epoch': '2.091'}
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| 22 |
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{'loss': '0.4411', 'grad_norm': '5.262', 'learning_rate': '1.057e-05', 'epoch': '2.098'}
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| 23 |
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{'loss': '0.4506', 'grad_norm': '4.764', 'learning_rate': '1.053e-05', 'epoch': '2.104'}
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| 24 |
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{'loss': '0.4066', 'grad_norm': '2.191', 'learning_rate': '1.05e-05', 'epoch': '2.111'}
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| 25 |
+
{'loss': '0.4325', 'grad_norm': '5.071', 'learning_rate': '1.046e-05', 'epoch': '2.117'}
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| 26 |
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{'loss': '0.4124', 'grad_norm': '5.363', 'learning_rate': '1.043e-05', 'epoch': '2.123'}
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| 27 |
+
{'loss': '0.4169', 'grad_norm': '3.958', 'learning_rate': '1.039e-05', 'epoch': '2.13'}
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| 28 |
+
{'loss': '0.4166', 'grad_norm': '2.913', 'learning_rate': '1.035e-05', 'epoch': '2.136'}
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| 29 |
+
2026-03-02 19:28:04 - TripletEvaluator: Evaluating the model on the dev-768 dataset in epoch 2.1364085133932185 after 66000 steps (truncated to 768):
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| 30 |
+
2026-03-02 19:49:48 - Accuracy Cosine Similarity: 98.05%
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| 31 |
+
2026-03-02 19:49:48 - TripletEvaluator: Evaluating the model on the dev-512 dataset in epoch 2.1364085133932185 after 66000 steps (truncated to 512):
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| 32 |
+
2026-03-02 20:10:59 - Accuracy Cosine Similarity: 98.06%
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| 33 |
+
2026-03-02 20:10:59 - TripletEvaluator: Evaluating the model on the dev-256 dataset in epoch 2.1364085133932185 after 66000 steps (truncated to 256):
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| 34 |
+
2026-03-02 20:32:00 - Accuracy Cosine Similarity: 98.06%
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| 35 |
+
2026-03-02 20:32:00 - TripletEvaluator: Evaluating the model on the dev-128 dataset in epoch 2.1364085133932185 after 66000 steps (truncated to 128):
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| 36 |
+
2026-03-02 20:52:44 - Accuracy Cosine Similarity: 98.03%
|
| 37 |
+
2026-03-02 20:52:44 - TripletEvaluator: Evaluating the model on the dev-64 dataset in epoch 2.1364085133932185 after 66000 steps (truncated to 64):
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| 38 |
+
2026-03-02 21:13:05 - Accuracy Cosine Similarity: 97.93%
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| 39 |
+
{'eval_train_loss': '0.2883', 'eval_dev-768_cosine_accuracy': '0.9805', 'eval_dev-512_cosine_accuracy': '0.9806', 'eval_dev-256_cosine_accuracy': '0.9806', 'eval_dev-128_cosine_accuracy': '0.9803', 'eval_dev-64_cosine_accuracy': '0.9793', 'eval_sequential_score': '0.9805', 'eval_train_runtime': '9366', 'eval_train_samples_per_second': '120.6', 'eval_train_steps_per_second': '15.08', 'epoch': '2.136'}
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| 40 |
+
2026-03-02 21:13:05 - Saving model checkpoint to /home/skiredj.abderrahman/khalil/sbert_training/output/arabert_20260224_1730/checkpoint-66000
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| 41 |
+
2026-03-02 21:13:05 - Save model to /home/skiredj.abderrahman/khalil/sbert_training/output/arabert_20260224_1730/checkpoint-66000
|
| 42 |
+
{'loss': '0.4069', 'grad_norm': '4.696', 'learning_rate': '1.032e-05', 'epoch': '2.143'}
|
| 43 |
+
{'loss': '0.4144', 'grad_norm': '4.022', 'learning_rate': '1.028e-05', 'epoch': '2.149'}
|
| 44 |
+
{'loss': '0.3948', 'grad_norm': '3.288', 'learning_rate': '1.025e-05', 'epoch': '2.156'}
|
| 45 |
+
{'loss': '0.4284', 'grad_norm': '2.968', 'learning_rate': '1.021e-05', 'epoch': '2.162'}
|
| 46 |
+
{'loss': '0.3984', 'grad_norm': '3.579', 'learning_rate': '1.017e-05', 'epoch': '2.169'}
|
| 47 |
+
{'loss': '0.4015', 'grad_norm': '5.276', 'learning_rate': '1.014e-05', 'epoch': '2.175'}
|
| 48 |
+
{'loss': '0.3999', 'grad_norm': '3.263', 'learning_rate': '1.01e-05', 'epoch': '2.182'}
|
| 49 |
+
{'loss': '0.397', 'grad_norm': '4.018', 'learning_rate': '1.007e-05', 'epoch': '2.188'}
|
| 50 |
+
{'loss': '0.3605', 'grad_norm': '3.304', 'learning_rate': '1.003e-05', 'epoch': '2.195'}
|
| 51 |
+
{'loss': '0.3851', 'grad_norm': '5.208', 'learning_rate': '9.994e-06', 'epoch': '2.201'}
|
| 52 |
+
{'loss': '0.397', 'grad_norm': '2.203', 'learning_rate': '9.958e-06', 'epoch': '2.208'}
|
| 53 |
+
{'loss': '0.3912', 'grad_norm': '3.873', 'learning_rate': '9.922e-06', 'epoch': '2.214'}
|
| 54 |
+
{'loss': '0.3772', 'grad_norm': '6.111', 'learning_rate': '9.886e-06', 'epoch': '2.221'}
|
| 55 |
+
{'loss': '0.3782', 'grad_norm': '4.189', 'learning_rate': '9.85e-06', 'epoch': '2.227'}
|
| 56 |
+
{'loss': '0.378', 'grad_norm': '4.917', 'learning_rate': '9.814e-06', 'epoch': '2.234'}
|
| 57 |
+
{'loss': '0.3605', 'grad_norm': '1.853', 'learning_rate': '9.778e-06', 'epoch': '2.24'}
|
| 58 |
+
{'loss': '0.362', 'grad_norm': '3.2', 'learning_rate': '9.742e-06', 'epoch': '2.246'}
|
| 59 |
+
{'loss': '0.3717', 'grad_norm': '4.389', 'learning_rate': '9.706e-06', 'epoch': '2.253'}
|
| 60 |
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{'loss': '0.3656', 'grad_norm': '2.031', 'learning_rate': '9.67e-06', 'epoch': '2.259'}
|
| 61 |
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{'loss': '0.3766', 'grad_norm': '5.601', 'learning_rate': '9.634e-06', 'epoch': '2.266'}
|
| 62 |
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{'loss': '0.3768', 'grad_norm': '4.343', 'learning_rate': '9.598e-06', 'epoch': '2.272'}
|
| 63 |
+
{'loss': '0.3853', 'grad_norm': '3.466', 'learning_rate': '9.562e-06', 'epoch': '2.279'}
|
| 64 |
+
{'loss': '0.3444', 'grad_norm': '3.306', 'learning_rate': '9.526e-06', 'epoch': '2.285'}
|
| 65 |
+
{'loss': '0.3863', 'grad_norm': '4.16', 'learning_rate': '9.49e-06', 'epoch': '2.292'}
|
| 66 |
+
{'loss': '0.3843', 'grad_norm': '2.997', 'learning_rate': '9.454e-06', 'epoch': '2.298'}
|
| 67 |
+
{'loss': '0.362', 'grad_norm': '3.893', 'learning_rate': '9.418e-06', 'epoch': '2.305'}
|
| 68 |
+
{'loss': '0.374', 'grad_norm': '4.415', 'learning_rate': '9.382e-06', 'epoch': '2.311'}
|
| 69 |
+
{'loss': '0.3657', 'grad_norm': '4.601', 'learning_rate': '9.346e-06', 'epoch': '2.318'}
|
| 70 |
+
{'loss': '0.3838', 'grad_norm': '3.083', 'learning_rate': '9.311e-06', 'epoch': '2.324'}
|
| 71 |
+
{'loss': '0.357', 'grad_norm': '5.532', 'learning_rate': '9.275e-06', 'epoch': '2.331'}
|
| 72 |
+
2026-03-02 23:01:58 - TripletEvaluator: Evaluating the model on the dev-768 dataset in epoch 2.3306304119122765 after 72000 steps (truncated to 768):
|
| 73 |
+
2026-03-02 23:22:39 - Accuracy Cosine Similarity: 98.15%
|
| 74 |
+
2026-03-02 23:22:39 - TripletEvaluator: Evaluating the model on the dev-512 dataset in epoch 2.3306304119122765 after 72000 steps (truncated to 512):
|
| 75 |
+
2026-03-02 23:43:44 - Accuracy Cosine Similarity: 98.16%
|
| 76 |
+
2026-03-02 23:43:44 - TripletEvaluator: Evaluating the model on the dev-256 dataset in epoch 2.3306304119122765 after 72000 steps (truncated to 256):
|
| 77 |
+
2026-03-03 00:04:58 - Accuracy Cosine Similarity: 98.17%
|
| 78 |
+
2026-03-03 00:04:58 - TripletEvaluator: Evaluating the model on the dev-128 dataset in epoch 2.3306304119122765 after 72000 steps (truncated to 128):
|
| 79 |
+
2026-03-03 00:25:36 - Accuracy Cosine Similarity: 98.13%
|
| 80 |
+
2026-03-03 00:25:36 - TripletEvaluator: Evaluating the model on the dev-64 dataset in epoch 2.3306304119122765 after 72000 steps (truncated to 64):
|
| 81 |
+
2026-03-03 00:46:23 - Accuracy Cosine Similarity: 98.01%
|
| 82 |
+
{'eval_train_loss': '0.2768', 'eval_dev-768_cosine_accuracy': '0.9815', 'eval_dev-512_cosine_accuracy': '0.9816', 'eval_dev-256_cosine_accuracy': '0.9817', 'eval_dev-128_cosine_accuracy': '0.9813', 'eval_dev-64_cosine_accuracy': '0.9801', 'eval_sequential_score': '0.9815', 'eval_train_runtime': '9309', 'eval_train_samples_per_second': '121.4', 'eval_train_steps_per_second': '15.17', 'epoch': '2.331'}
|
| 83 |
+
2026-03-03 00:46:23 - Saving model checkpoint to /home/skiredj.abderrahman/khalil/sbert_training/output/arabert_20260224_1730/checkpoint-72000
|
| 84 |
+
2026-03-03 00:46:23 - Save model to /home/skiredj.abderrahman/khalil/sbert_training/output/arabert_20260224_1730/checkpoint-72000
|
| 85 |
+
{'loss': '0.3562', 'grad_norm': '4.382', 'learning_rate': '9.239e-06', 'epoch': '2.337'}
|
| 86 |
+
{'loss': '0.3854', 'grad_norm': '5.293', 'learning_rate': '9.203e-06', 'epoch': '2.344'}
|
| 87 |
+
{'loss': '0.3574', 'grad_norm': '5.037', 'learning_rate': '9.167e-06', 'epoch': '2.35'}
|
| 88 |
+
{'loss': '0.3789', 'grad_norm': '3.6', 'learning_rate': '9.131e-06', 'epoch': '2.357'}
|
| 89 |
+
{'loss': '0.3708', 'grad_norm': '4.49', 'learning_rate': '9.095e-06', 'epoch': '2.363'}
|
| 90 |
+
{'loss': '0.3688', 'grad_norm': '4.346', 'learning_rate': '9.059e-06', 'epoch': '2.369'}
|
| 91 |
+
{'loss': '0.3764', 'grad_norm': '3.589', 'learning_rate': '9.023e-06', 'epoch': '2.376'}
|
| 92 |
+
{'loss': '0.3776', 'grad_norm': '5.037', 'learning_rate': '8.987e-06', 'epoch': '2.382'}
|
| 93 |
+
{'loss': '0.3752', 'grad_norm': '3.274', 'learning_rate': '8.951e-06', 'epoch': '2.389'}
|
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+
{'loss': '0.3575', 'grad_norm': '3.484', 'learning_rate': '8.915e-06', 'epoch': '2.395'}
|
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+
{'loss': '0.356', 'grad_norm': '4.98', 'learning_rate': '8.879e-06', 'epoch': '2.402'}
|
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+
{'loss': '0.3858', 'grad_norm': '3.007', 'learning_rate': '8.843e-06', 'epoch': '2.408'}
|
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+
{'loss': '0.3644', 'grad_norm': '1.843', 'learning_rate': '8.807e-06', 'epoch': '2.415'}
|
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+
{'loss': '0.394', 'grad_norm': '2.967', 'learning_rate': '8.771e-06', 'epoch': '2.421'}
|
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+
{'loss': '0.3776', 'grad_norm': '4.421', 'learning_rate': '8.735e-06', 'epoch': '2.428'}
|
| 100 |
+
{'loss': '0.3467', 'grad_norm': '4.17', 'learning_rate': '8.699e-06', 'epoch': '2.434'}
|
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+
{'loss': '0.3651', 'grad_norm': '4.477', 'learning_rate': '8.663e-06', 'epoch': '2.441'}
|
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+
{'loss': '0.3865', 'grad_norm': '4.592', 'learning_rate': '8.627e-06', 'epoch': '2.447'}
|
| 103 |
+
{'loss': '0.3856', 'grad_norm': '2.638', 'learning_rate': '8.591e-06', 'epoch': '2.454'}
|
| 104 |
+
{'loss': '0.3693', 'grad_norm': '4.006', 'learning_rate': '8.555e-06', 'epoch': '2.46'}
|
| 105 |
+
{'loss': '0.361', 'grad_norm': '3.124', 'learning_rate': '8.519e-06', 'epoch': '2.467'}
|
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+
{'loss': '0.3693', 'grad_norm': '3.233', 'learning_rate': '8.483e-06', 'epoch': '2.473'}
|
| 107 |
+
{'loss': '0.3708', 'grad_norm': '3.78', 'learning_rate': '8.447e-06', 'epoch': '2.48'}
|
| 108 |
+
{'loss': '0.3548', 'grad_norm': '3.921', 'learning_rate': '8.411e-06', 'epoch': '2.486'}
|
| 109 |
+
{'loss': '0.3767', 'grad_norm': '5.192', 'learning_rate': '8.375e-06', 'epoch': '2.492'}
|
| 110 |
+
{'loss': '0.3668', 'grad_norm': '3.988', 'learning_rate': '8.339e-06', 'epoch': '2.499'}
|
| 111 |
+
{'loss': '0.3734', 'grad_norm': '3.967', 'learning_rate': '8.303e-06', 'epoch': '2.505'}
|
| 112 |
+
{'loss': '0.3563', 'grad_norm': '2.322', 'learning_rate': '8.267e-06', 'epoch': '2.512'}
|
| 113 |
+
{'loss': '0.3842', 'grad_norm': '3.582', 'learning_rate': '8.232e-06', 'epoch': '2.518'}
|
| 114 |
+
{'loss': '0.3541', 'grad_norm': '5.197', 'learning_rate': '8.196e-06', 'epoch': '2.525'}
|
| 115 |
+
2026-03-03 02:35:54 - TripletEvaluator: Evaluating the model on the dev-768 dataset in epoch 2.5248523104313345 after 78000 steps (truncated to 768):
|
| 116 |
+
2026-03-03 02:56:30 - Accuracy Cosine Similarity: 98.20%
|
| 117 |
+
2026-03-03 02:56:30 - TripletEvaluator: Evaluating the model on the dev-512 dataset in epoch 2.5248523104313345 after 78000 steps (truncated to 512):
|
| 118 |
+
2026-03-03 03:17:13 - Accuracy Cosine Similarity: 98.21%
|
| 119 |
+
2026-03-03 03:17:13 - TripletEvaluator: Evaluating the model on the dev-256 dataset in epoch 2.5248523104313345 after 78000 steps (truncated to 256):
|
| 120 |
+
2026-03-03 03:38:15 - Accuracy Cosine Similarity: 98.22%
|
| 121 |
+
2026-03-03 03:38:15 - TripletEvaluator: Evaluating the model on the dev-128 dataset in epoch 2.5248523104313345 after 78000 steps (truncated to 128):
|
| 122 |
+
2026-03-03 03:59:20 - Accuracy Cosine Similarity: 98.18%
|
| 123 |
+
2026-03-03 03:59:20 - TripletEvaluator: Evaluating the model on the dev-64 dataset in epoch 2.5248523104313345 after 78000 steps (truncated to 64):
|
| 124 |
+
2026-03-03 04:19:52 - Accuracy Cosine Similarity: 98.10%
|
| 125 |
+
{'eval_train_loss': '0.2668', 'eval_dev-768_cosine_accuracy': '0.982', 'eval_dev-512_cosine_accuracy': '0.9821', 'eval_dev-256_cosine_accuracy': '0.9822', 'eval_dev-128_cosine_accuracy': '0.9818', 'eval_dev-64_cosine_accuracy': '0.981', 'eval_sequential_score': '0.982', 'eval_train_runtime': '9281', 'eval_train_samples_per_second': '121.7', 'eval_train_steps_per_second': '15.22', 'epoch': '2.525'}
|
| 126 |
+
2026-03-03 04:19:52 - Saving model checkpoint to /home/skiredj.abderrahman/khalil/sbert_training/output/arabert_20260224_1730/checkpoint-78000
|
| 127 |
+
2026-03-03 04:19:52 - Save model to /home/skiredj.abderrahman/khalil/sbert_training/output/arabert_20260224_1730/checkpoint-78000
|
| 128 |
+
{'loss': '0.3692', 'grad_norm': '5.604', 'learning_rate': '8.16e-06', 'epoch': '2.531'}
|
| 129 |
+
{'loss': '0.3609', 'grad_norm': '4.981', 'learning_rate': '8.124e-06', 'epoch': '2.538'}
|
| 130 |
+
{'loss': '0.3763', 'grad_norm': '5.883', 'learning_rate': '8.088e-06', 'epoch': '2.544'}
|
| 131 |
+
{'loss': '0.3684', 'grad_norm': '2.069', 'learning_rate': '8.052e-06', 'epoch': '2.551'}
|
| 132 |
+
{'loss': '0.3614', 'grad_norm': '4.359', 'learning_rate': '8.016e-06', 'epoch': '2.557'}
|
| 133 |
+
{'loss': '0.3789', 'grad_norm': '3.143', 'learning_rate': '7.98e-06', 'epoch': '2.564'}
|
| 134 |
+
{'loss': '0.3689', 'grad_norm': '3.077', 'learning_rate': '7.944e-06', 'epoch': '2.57'}
|
| 135 |
+
{'loss': '0.3745', 'grad_norm': '2.937', 'learning_rate': '7.908e-06', 'epoch': '2.577'}
|
| 136 |
+
{'loss': '0.3725', 'grad_norm': '5.283', 'learning_rate': '7.872e-06', 'epoch': '2.583'}
|
| 137 |
+
{'loss': '0.3654', 'grad_norm': '3.622', 'learning_rate': '7.836e-06', 'epoch': '2.59'}
|
| 138 |
+
{'loss': '0.3653', 'grad_norm': '4.478', 'learning_rate': '7.8e-06', 'epoch': '2.596'}
|
| 139 |
+
{'loss': '0.3898', 'grad_norm': '4.282', 'learning_rate': '7.764e-06', 'epoch': '2.603'}
|
| 140 |
+
{'loss': '0.3699', 'grad_norm': '5.231', 'learning_rate': '7.728e-06', 'epoch': '2.609'}
|
| 141 |
+
{'loss': '0.3734', 'grad_norm': '4.661', 'learning_rate': '7.692e-06', 'epoch': '2.615'}
|
| 142 |
+
{'loss': '0.3575', 'grad_norm': '5.115', 'learning_rate': '7.656e-06', 'epoch': '2.622'}
|
| 143 |
+
{'loss': '0.3776', 'grad_norm': '3.268', 'learning_rate': '7.62e-06', 'epoch': '2.628'}
|
| 144 |
+
{'loss': '0.3764', 'grad_norm': '3.383', 'learning_rate': '7.584e-06', 'epoch': '2.635'}
|
| 145 |
+
{'loss': '0.3692', 'grad_norm': '5.811', 'learning_rate': '7.548e-06', 'epoch': '2.641'}
|
| 146 |
+
{'loss': '0.3573', 'grad_norm': '3.134', 'learning_rate': '7.512e-06', 'epoch': '2.648'}
|
| 147 |
+
{'loss': '0.3535', 'grad_norm': '3.569', 'learning_rate': '7.476e-06', 'epoch': '2.654'}
|
| 148 |
+
{'loss': '0.3686', 'grad_norm': '5.449', 'learning_rate': '7.44e-06', 'epoch': '2.661'}
|
| 149 |
+
{'loss': '0.3763', 'grad_norm': '2.932', 'learning_rate': '7.404e-06', 'epoch': '2.667'}
|
| 150 |
+
{'loss': '0.3589', 'grad_norm': '4.469', 'learning_rate': '7.368e-06', 'epoch': '2.674'}
|
| 151 |
+
{'loss': '0.3764', 'grad_norm': '3.381', 'learning_rate': '7.332e-06', 'epoch': '2.68'}
|
| 152 |
+
{'loss': '0.3682', 'grad_norm': '4.811', 'learning_rate': '7.296e-06', 'epoch': '2.687'}
|
| 153 |
+
{'loss': '0.3618', 'grad_norm': '4.508', 'learning_rate': '7.26e-06', 'epoch': '2.693'}
|
| 154 |
+
{'loss': '0.3722', 'grad_norm': '3.616', 'learning_rate': '7.224e-06', 'epoch': '2.7'}
|
| 155 |
+
{'loss': '0.4015', 'grad_norm': '4.274', 'learning_rate': '7.188e-06', 'epoch': '2.706'}
|
| 156 |
+
{'loss': '0.3673', 'grad_norm': '4.678', 'learning_rate': '7.153e-06', 'epoch': '2.713'}
|
| 157 |
+
{'loss': '0.3715', 'grad_norm': '4.915', 'learning_rate': '7.117e-06', 'epoch': '2.719'}
|
| 158 |
+
2026-03-03 06:09:38 - TripletEvaluator: Evaluating the model on the dev-768 dataset in epoch 2.7190742089503925 after 84000 steps (truncated to 768):
|
| 159 |
+
2026-03-03 06:30:51 - Accuracy Cosine Similarity: 98.31%
|
| 160 |
+
2026-03-03 06:30:51 - TripletEvaluator: Evaluating the model on the dev-512 dataset in epoch 2.7190742089503925 after 84000 steps (truncated to 512):
|
| 161 |
+
2026-03-03 06:52:09 - Accuracy Cosine Similarity: 98.32%
|
| 162 |
+
2026-03-03 06:52:09 - TripletEvaluator: Evaluating the model on the dev-256 dataset in epoch 2.7190742089503925 after 84000 steps (truncated to 256):
|
| 163 |
+
2026-03-03 07:13:25 - Accuracy Cosine Similarity: 98.32%
|
| 164 |
+
2026-03-03 07:13:25 - TripletEvaluator: Evaluating the model on the dev-128 dataset in epoch 2.7190742089503925 after 84000 steps (truncated to 128):
|
| 165 |
+
2026-03-03 07:34:32 - Accuracy Cosine Similarity: 98.29%
|
| 166 |
+
2026-03-03 07:34:32 - TripletEvaluator: Evaluating the model on the dev-64 dataset in epoch 2.7190742089503925 after 84000 steps (truncated to 64):
|
| 167 |
+
2026-03-03 07:55:19 - Accuracy Cosine Similarity: 98.20%
|
| 168 |
+
{'eval_train_loss': '0.2525', 'eval_dev-768_cosine_accuracy': '0.9831', 'eval_dev-512_cosine_accuracy': '0.9832', 'eval_dev-256_cosine_accuracy': '0.9832', 'eval_dev-128_cosine_accuracy': '0.9829', 'eval_dev-64_cosine_accuracy': '0.982', 'eval_sequential_score': '0.9831', 'eval_train_runtime': '9385', 'eval_train_samples_per_second': '120.4', 'eval_train_steps_per_second': '15.05', 'epoch': '2.719'}
|
| 169 |
+
2026-03-03 07:55:19 - Saving model checkpoint to /home/skiredj.abderrahman/khalil/sbert_training/output/arabert_20260224_1730/checkpoint-84000
|
| 170 |
+
2026-03-03 07:55:19 - Save model to /home/skiredj.abderrahman/khalil/sbert_training/output/arabert_20260224_1730/checkpoint-84000
|
| 171 |
+
{'loss': '0.3694', 'grad_norm': '2.933', 'learning_rate': '7.081e-06', 'epoch': '2.726'}
|
| 172 |
+
{'loss': '0.365', 'grad_norm': '4.5', 'learning_rate': '7.045e-06', 'epoch': '2.732'}
|
| 173 |
+
{'loss': '0.3753', 'grad_norm': '4.921', 'learning_rate': '7.009e-06', 'epoch': '2.738'}
|
| 174 |
+
{'loss': '0.3554', 'grad_norm': '3.083', 'learning_rate': '6.973e-06', 'epoch': '2.745'}
|
| 175 |
+
{'loss': '0.3936', 'grad_norm': '5.051', 'learning_rate': '6.937e-06', 'epoch': '2.751'}
|
| 176 |
+
{'loss': '0.3735', 'grad_norm': '6.889', 'learning_rate': '6.901e-06', 'epoch': '2.758'}
|
| 177 |
+
{'loss': '0.3746', 'grad_norm': '3.799', 'learning_rate': '6.865e-06', 'epoch': '2.764'}
|
| 178 |
+
{'loss': '0.3619', 'grad_norm': '2.101', 'learning_rate': '6.829e-06', 'epoch': '2.771'}
|
| 179 |
+
{'loss': '0.3705', 'grad_norm': '4.39', 'learning_rate': '6.793e-06', 'epoch': '2.777'}
|
| 180 |
+
{'loss': '0.359', 'grad_norm': '2.37', 'learning_rate': '6.757e-06', 'epoch': '2.784'}
|
| 181 |
+
{'loss': '0.3682', 'grad_norm': '3.344', 'learning_rate': '6.721e-06', 'epoch': '2.79'}
|
| 182 |
+
{'loss': '0.3821', 'grad_norm': '3.043', 'learning_rate': '6.685e-06', 'epoch': '2.797'}
|
| 183 |
+
{'loss': '0.3624', 'grad_norm': '3.74', 'learning_rate': '6.649e-06', 'epoch': '2.803'}
|
| 184 |
+
{'loss': '0.3751', 'grad_norm': '3.94', 'learning_rate': '6.613e-06', 'epoch': '2.81'}
|
| 185 |
+
{'loss': '0.3955', 'grad_norm': '4.282', 'learning_rate': '6.577e-06', 'epoch': '2.816'}
|
| 186 |
+
{'loss': '0.3581', 'grad_norm': '4.607', 'learning_rate': '6.541e-06', 'epoch': '2.823'}
|
| 187 |
+
{'loss': '0.3588', 'grad_norm': '5.26', 'learning_rate': '6.505e-06', 'epoch': '2.829'}
|
| 188 |
+
{'loss': '0.3622', 'grad_norm': '4.333', 'learning_rate': '6.469e-06', 'epoch': '2.836'}
|
| 189 |
+
{'loss': '0.375', 'grad_norm': '3.19', 'learning_rate': '6.433e-06', 'epoch': '2.842'}
|
| 190 |
+
{'loss': '0.3589', 'grad_norm': '3.558', 'learning_rate': '6.397e-06', 'epoch': '2.849'}
|
| 191 |
+
{'loss': '0.3816', 'grad_norm': '2.926', 'learning_rate': '6.361e-06', 'epoch': '2.855'}
|
| 192 |
+
{'loss': '0.3624', 'grad_norm': '5.621', 'learning_rate': '6.325e-06', 'epoch': '2.862'}
|
| 193 |
+
{'loss': '0.3923', 'grad_norm': '4.943', 'learning_rate': '6.289e-06', 'epoch': '2.868'}
|
| 194 |
+
{'loss': '0.3798', 'grad_norm': '5.76', 'learning_rate': '6.253e-06', 'epoch': '2.874'}
|
| 195 |
+
{'loss': '0.3362', 'grad_norm': '2.747', 'learning_rate': '6.217e-06', 'epoch': '2.881'}
|
| 196 |
+
{'loss': '0.3766', 'grad_norm': '4.018', 'learning_rate': '6.181e-06', 'epoch': '2.887'}
|
| 197 |
+
{'loss': '0.3839', 'grad_norm': '4.481', 'learning_rate': '6.145e-06', 'epoch': '2.894'}
|
| 198 |
+
{'loss': '0.376', 'grad_norm': '3.558', 'learning_rate': '6.109e-06', 'epoch': '2.9'}
|
| 199 |
+
{'loss': '0.3958', 'grad_norm': '3.886', 'learning_rate': '6.074e-06', 'epoch': '2.907'}
|
| 200 |
+
{'loss': '0.372', 'grad_norm': '3.935', 'learning_rate': '6.038e-06', 'epoch': '2.913'}
|
| 201 |
+
2026-03-03 09:45:15 - TripletEvaluator: Evaluating the model on the dev-768 dataset in epoch 2.9132961074694506 after 90000 steps (truncated to 768):
|
| 202 |
+
2026-03-03 10:06:33 - Accuracy Cosine Similarity: 98.36%
|
| 203 |
+
2026-03-03 10:06:33 - TripletEvaluator: Evaluating the model on the dev-512 dataset in epoch 2.9132961074694506 after 90000 steps (truncated to 512):
|
| 204 |
+
2026-03-03 10:27:44 - Accuracy Cosine Similarity: 98.38%
|
| 205 |
+
2026-03-03 10:27:44 - TripletEvaluator: Evaluating the model on the dev-256 dataset in epoch 2.9132961074694506 after 90000 steps (truncated to 256):
|
| 206 |
+
2026-03-03 10:48:39 - Accuracy Cosine Similarity: 98.40%
|
| 207 |
+
2026-03-03 10:48:39 - TripletEvaluator: Evaluating the model on the dev-128 dataset in epoch 2.9132961074694506 after 90000 steps (truncated to 128):
|
| 208 |
+
2026-03-03 11:09:11 - Accuracy Cosine Similarity: 98.35%
|
| 209 |
+
2026-03-03 11:09:11 - TripletEvaluator: Evaluating the model on the dev-64 dataset in epoch 2.9132961074694506 after 90000 steps (truncated to 64):
|
| 210 |
+
2026-03-03 11:30:20 - Accuracy Cosine Similarity: 98.26%
|
| 211 |
+
{'eval_train_loss': '0.243', 'eval_dev-768_cosine_accuracy': '0.9836', 'eval_dev-512_cosine_accuracy': '0.9838', 'eval_dev-256_cosine_accuracy': '0.984', 'eval_dev-128_cosine_accuracy': '0.9835', 'eval_dev-64_cosine_accuracy': '0.9826', 'eval_sequential_score': '0.9836', 'eval_train_runtime': '9339', 'eval_train_samples_per_second': '121', 'eval_train_steps_per_second': '15.12', 'epoch': '2.913'}
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2026-03-03 11:30:20 - Saving model checkpoint to /home/skiredj.abderrahman/khalil/sbert_training/output/arabert_20260224_1730/checkpoint-90000
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2026-03-03 11:30:20 - Save model to /home/skiredj.abderrahman/khalil/sbert_training/output/arabert_20260224_1730/checkpoint-90000
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{'loss': '0.3724', 'grad_norm': '4.557', 'learning_rate': '6.002e-06', 'epoch': '2.92'}
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{'loss': '0.3708', 'grad_norm': '5.298', 'learning_rate': '5.966e-06', 'epoch': '2.926'}
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{'loss': '0.3804', 'grad_norm': '4.007', 'learning_rate': '5.93e-06', 'epoch': '2.933'}
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{'loss': '0.3647', 'grad_norm': '6.184', 'learning_rate': '5.894e-06', 'epoch': '2.939'}
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{'loss': '0.3721', 'grad_norm': '2.113', 'learning_rate': '5.858e-06', 'epoch': '2.946'}
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{'loss': '0.389', 'grad_norm': '4.014', 'learning_rate': '5.822e-06', 'epoch': '2.952'}
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{'loss': '0.4107', 'grad_norm': '4.331', 'learning_rate': '5.786e-06', 'epoch': '2.959'}
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{'loss': '0.3917', 'grad_norm': '5.432', 'learning_rate': '5.75e-06', 'epoch': '2.965'}
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{'loss': '0.3608', 'grad_norm': '5.648', 'learning_rate': '5.714e-06', 'epoch': '2.972'}
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{'loss': '0.386', 'grad_norm': '3.361', 'learning_rate': '5.678e-06', 'epoch': '2.978'}
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{'loss': '0.3998', 'grad_norm': '4.219', 'learning_rate': '5.642e-06', 'epoch': '2.985'}
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{'loss': '0.3889', 'grad_norm': '4.905', 'learning_rate': '5.606e-06', 'epoch': '2.991'}
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{'loss': '0.3715', 'grad_norm': '5.136', 'learning_rate': '5.57e-06', 'epoch': '2.997'}
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{'loss': '0.345', 'grad_norm': '4.286', 'learning_rate': '5.534e-06', 'epoch': '3.004'}
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{'loss': '0.2915', 'grad_norm': '4.063', 'learning_rate': '5.498e-06', 'epoch': '3.01'}
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{'loss': '0.2876', 'grad_norm': '5.342', 'learning_rate': '5.462e-06', 'epoch': '3.017'}
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{'loss': '0.2979', 'grad_norm': '2.142', 'learning_rate': '5.426e-06', 'epoch': '3.023'}
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{'loss': '0.2952', 'grad_norm': '2.973', 'learning_rate': '5.39e-06', 'epoch': '3.03'}
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{'loss': '0.2925', 'grad_norm': '5.659', 'learning_rate': '5.354e-06', 'epoch': '3.036'}
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{'loss': '0.2944', 'grad_norm': '4.593', 'learning_rate': '5.318e-06', 'epoch': '3.043'}
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{'loss': '0.3033', 'grad_norm': '5.093', 'learning_rate': '5.282e-06', 'epoch': '3.049'}
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{'loss': '0.2963', 'grad_norm': '3.233', 'learning_rate': '5.246e-06', 'epoch': '3.056'}
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{'loss': '0.2835', 'grad_norm': '5.186', 'learning_rate': '5.21e-06', 'epoch': '3.062'}
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{'loss': '0.2987', 'grad_norm': '3.581', 'learning_rate': '5.174e-06', 'epoch': '3.069'}
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{'loss': '0.3', 'grad_norm': '4.349', 'learning_rate': '5.138e-06', 'epoch': '3.075'}
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{'loss': '0.2845', 'grad_norm': '4.465', 'learning_rate': '5.102e-06', 'epoch': '3.082'}
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{'loss': '0.2899', 'grad_norm': '3.619', 'learning_rate': '5.066e-06', 'epoch': '3.088'}
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{'loss': '0.3078', 'grad_norm': '3.873', 'learning_rate': '5.03e-06', 'epoch': '3.095'}
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{'loss': '0.2943', 'grad_norm': '4.048', 'learning_rate': '4.995e-06', 'epoch': '3.101'}
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{'loss': '0.2758', 'grad_norm': '3.275', 'learning_rate': '4.959e-06', 'epoch': '3.108'}
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2026-03-03 13:20:03 - TripletEvaluator: Evaluating the model on the dev-768 dataset in epoch 3.1075018208302985 after 96000 steps (truncated to 768):
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2026-03-03 13:42:30 - Accuracy Cosine Similarity: 98.41%
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2026-03-03 13:42:30 - TripletEvaluator: Evaluating the model on the dev-512 dataset in epoch 3.1075018208302985 after 96000 steps (truncated to 512):
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2026-03-03 14:05:12 - Accuracy Cosine Similarity: 98.42%
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2026-03-03 14:05:12 - TripletEvaluator: Evaluating the model on the dev-256 dataset in epoch 3.1075018208302985 after 96000 steps (truncated to 256):
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2026-03-03 14:27:36 - Accuracy Cosine Similarity: 98.44%
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2026-03-03 14:27:36 - TripletEvaluator: Evaluating the model on the dev-128 dataset in epoch 3.1075018208302985 after 96000 steps (truncated to 128):
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+
2026-03-03 14:50:20 - Accuracy Cosine Similarity: 98.42%
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2026-03-03 14:50:20 - TripletEvaluator: Evaluating the model on the dev-64 dataset in epoch 3.1075018208302985 after 96000 steps (truncated to 64):
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2026-03-03 15:12:47 - Accuracy Cosine Similarity: 98.34%
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| 254 |
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{'eval_train_loss': '0.2361', 'eval_dev-768_cosine_accuracy': '0.9841', 'eval_dev-512_cosine_accuracy': '0.9842', 'eval_dev-256_cosine_accuracy': '0.9844', 'eval_dev-128_cosine_accuracy': '0.9842', 'eval_dev-64_cosine_accuracy': '0.9834', 'eval_sequential_score': '0.9841', 'eval_train_runtime': '9833', 'eval_train_samples_per_second': '114.9', 'eval_train_steps_per_second': '14.36', 'epoch': '3.108'}
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2026-03-03 15:12:47 - Saving model checkpoint to /home/skiredj.abderrahman/khalil/sbert_training/output/arabert_20260224_1730/checkpoint-96000
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2026-03-03 15:12:47 - Save model to /home/skiredj.abderrahman/khalil/sbert_training/output/arabert_20260224_1730/checkpoint-96000
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{'loss': '0.2835', 'grad_norm': '3.412', 'learning_rate': '4.923e-06', 'epoch': '3.114'}
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{'loss': '0.2774', 'grad_norm': '2.313', 'learning_rate': '4.887e-06', 'epoch': '3.12'}
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{'loss': '0.2696', 'grad_norm': '1.394', 'learning_rate': '4.851e-06', 'epoch': '3.127'}
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{'loss': '0.282', 'grad_norm': '4.947', 'learning_rate': '4.815e-06', 'epoch': '3.133'}
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{'loss': '0.2813', 'grad_norm': '3.528', 'learning_rate': '4.779e-06', 'epoch': '3.14'}
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{'loss': '0.2699', 'grad_norm': '3.904', 'learning_rate': '4.743e-06', 'epoch': '3.146'}
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{'loss': '0.2727', 'grad_norm': '3.14', 'learning_rate': '4.707e-06', 'epoch': '3.153'}
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{'loss': '0.2835', 'grad_norm': '3.664', 'learning_rate': '4.671e-06', 'epoch': '3.159'}
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{'loss': '0.2722', 'grad_norm': '2.961', 'learning_rate': '4.635e-06', 'epoch': '3.166'}
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{'loss': '0.2727', 'grad_norm': '4.693', 'learning_rate': '4.599e-06', 'epoch': '3.172'}
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{'loss': '0.2747', 'grad_norm': '2.871', 'learning_rate': '4.563e-06', 'epoch': '3.179'}
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{'loss': '0.2606', 'grad_norm': '3.735', 'learning_rate': '4.527e-06', 'epoch': '3.185'}
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{'loss': '0.2705', 'grad_norm': '1.348', 'learning_rate': '4.491e-06', 'epoch': '3.192'}
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{'loss': '0.2542', 'grad_norm': '5.111', 'learning_rate': '4.455e-06', 'epoch': '3.198'}
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{'loss': '0.2632', 'grad_norm': '5.231', 'learning_rate': '4.419e-06', 'epoch': '3.205'}
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{'loss': '0.2584', 'grad_norm': '2.372', 'learning_rate': '4.383e-06', 'epoch': '3.211'}
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{'loss': '0.2597', 'grad_norm': '2.103', 'learning_rate': '4.347e-06', 'epoch': '3.218'}
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{'loss': '0.2501', 'grad_norm': '4.595', 'learning_rate': '4.311e-06', 'epoch': '3.224'}
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{'loss': '0.2657', 'grad_norm': '5.29', 'learning_rate': '4.275e-06', 'epoch': '3.231'}
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{'loss': '0.2422', 'grad_norm': '1.912', 'learning_rate': '4.239e-06', 'epoch': '3.237'}
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{'loss': '0.2478', 'grad_norm': '2.928', 'learning_rate': '4.203e-06', 'epoch': '3.243'}
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{'loss': '0.2542', 'grad_norm': '4.825', 'learning_rate': '4.167e-06', 'epoch': '3.25'}
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{'loss': '0.2519', 'grad_norm': '3.828', 'learning_rate': '4.131e-06', 'epoch': '3.256'}
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{'loss': '0.2543', 'grad_norm': '2.029', 'learning_rate': '4.095e-06', 'epoch': '3.263'}
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{'loss': '0.2525', 'grad_norm': '3.603', 'learning_rate': '4.059e-06', 'epoch': '3.269'}
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{'loss': '0.2518', 'grad_norm': '2.854', 'learning_rate': '4.023e-06', 'epoch': '3.276'}
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{'loss': '0.2536', 'grad_norm': '2.543', 'learning_rate': '3.987e-06', 'epoch': '3.282'}
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{'loss': '0.253', 'grad_norm': '1.443', 'learning_rate': '3.951e-06', 'epoch': '3.289'}
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{'loss': '0.27', 'grad_norm': '3.106', 'learning_rate': '3.916e-06', 'epoch': '3.295'}
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{'loss': '0.2439', 'grad_norm': '2.519', 'learning_rate': '3.88e-06', 'epoch': '3.302'}
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2026-03-03 17:02:00 - TripletEvaluator: Evaluating the model on the dev-768 dataset in epoch 3.3017237193493565 after 102000 steps (truncated to 768):
|
| 288 |
+
2026-03-03 17:24:36 - Accuracy Cosine Similarity: 98.45%
|
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+
2026-03-03 17:24:36 - TripletEvaluator: Evaluating the model on the dev-512 dataset in epoch 3.3017237193493565 after 102000 steps (truncated to 512):
|
| 290 |
+
2026-03-03 17:47:01 - Accuracy Cosine Similarity: 98.46%
|
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+
2026-03-03 17:47:01 - TripletEvaluator: Evaluating the model on the dev-256 dataset in epoch 3.3017237193493565 after 102000 steps (truncated to 256):
|
| 292 |
+
2026-03-03 18:09:20 - Accuracy Cosine Similarity: 98.48%
|
| 293 |
+
2026-03-03 18:09:20 - TripletEvaluator: Evaluating the model on the dev-128 dataset in epoch 3.3017237193493565 after 102000 steps (truncated to 128):
|
| 294 |
+
2026-03-03 18:31:48 - Accuracy Cosine Similarity: 98.46%
|
| 295 |
+
2026-03-03 18:31:48 - TripletEvaluator: Evaluating the model on the dev-64 dataset in epoch 3.3017237193493565 after 102000 steps (truncated to 64):
|
| 296 |
+
2026-03-03 18:53:45 - Accuracy Cosine Similarity: 98.36%
|
| 297 |
+
{'eval_train_loss': '0.2297', 'eval_dev-768_cosine_accuracy': '0.9845', 'eval_dev-512_cosine_accuracy': '0.9846', 'eval_dev-256_cosine_accuracy': '0.9848', 'eval_dev-128_cosine_accuracy': '0.9846', 'eval_dev-64_cosine_accuracy': '0.9836', 'eval_sequential_score': '0.9845', 'eval_train_runtime': '9774', 'eval_train_samples_per_second': '115.6', 'eval_train_steps_per_second': '14.45', 'epoch': '3.302'}
|
| 298 |
+
2026-03-03 18:53:45 - Saving model checkpoint to /home/skiredj.abderrahman/khalil/sbert_training/output/arabert_20260224_1730/checkpoint-102000
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| 299 |
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2026-03-03 18:53:45 - Save model to /home/skiredj.abderrahman/khalil/sbert_training/output/arabert_20260224_1730/checkpoint-102000
|
| 300 |
+
{'loss': '0.2592', 'grad_norm': '3.317', 'learning_rate': '3.844e-06', 'epoch': '3.308'}
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{'loss': '0.2542', 'grad_norm': '1.975', 'learning_rate': '3.808e-06', 'epoch': '3.315'}
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{'loss': '0.2641', 'grad_norm': '4.947', 'learning_rate': '3.772e-06', 'epoch': '3.321'}
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{'loss': '0.2435', 'grad_norm': '2.649', 'learning_rate': '3.736e-06', 'epoch': '3.328'}
|
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{'loss': '0.251', 'grad_norm': '3.429', 'learning_rate': '3.7e-06', 'epoch': '3.334'}
|
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{'loss': '0.2481', 'grad_norm': '2.566', 'learning_rate': '3.664e-06', 'epoch': '3.341'}
|
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{'loss': '0.2628', 'grad_norm': '2.595', 'learning_rate': '3.628e-06', 'epoch': '3.347'}
|
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+
{'loss': '0.248', 'grad_norm': '2.943', 'learning_rate': '3.592e-06', 'epoch': '3.354'}
|
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2026-03-04 15:01:24 - Load pretrained SentenceTransformer: bert-base-arabertv02
|
| 309 |
+
2026-03-04 15:01:34 - '[Errno -2] Name or service not known' thrown while requesting HEAD https://huggingface.co/bert-base-arabertv02/resolve/main/./modules.json
|
| 310 |
+
2026-03-04 15:01:34 - Retrying in 1s [Retry 1/5].
|
| 311 |
+
2026-03-04 15:01:35 - No sentence-transformers model found with name bert-base-arabertv02. Creating a new one with mean pooling.
|
| 312 |
+
2026-03-04 15:03:06 - Use pytorch device_name: cuda:0
|
| 313 |
+
2026-03-04 15:03:06 - Load pretrained SentenceTransformer: /home/skiredj.abderrahman/khalil/sbert_training/output/arabert_20260224_1730/checkpoint-102000
|
| 314 |
+
{'loss': '0.2592', 'grad_norm': '3.323', 'learning_rate': '3.844e-06', 'epoch': '3.308'}
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{'loss': '0.2543', 'grad_norm': '1.973', 'learning_rate': '3.808e-06', 'epoch': '3.315'}
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{'loss': '0.2641', 'grad_norm': '4.95', 'learning_rate': '3.772e-06', 'epoch': '3.321'}
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{'loss': '0.2435', 'grad_norm': '2.661', 'learning_rate': '3.736e-06', 'epoch': '3.328'}
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{'loss': '0.251', 'grad_norm': '3.434', 'learning_rate': '3.7e-06', 'epoch': '3.334'}
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{'loss': '0.2481', 'grad_norm': '2.562', 'learning_rate': '3.664e-06', 'epoch': '3.341'}
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{'loss': '0.2627', 'grad_norm': '2.6', 'learning_rate': '3.628e-06', 'epoch': '3.347'}
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{'loss': '0.248', 'grad_norm': '2.941', 'learning_rate': '3.592e-06', 'epoch': '3.354'}
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{'loss': '0.2636', 'grad_norm': '2.153', 'learning_rate': '3.556e-06', 'epoch': '3.36'}
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{'loss': '0.2619', 'grad_norm': '2.729', 'learning_rate': '3.52e-06', 'epoch': '3.366'}
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{'loss': '0.2423', 'grad_norm': '2.413', 'learning_rate': '3.484e-06', 'epoch': '3.373'}
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{'loss': '0.2505', 'grad_norm': '4.396', 'learning_rate': '3.448e-06', 'epoch': '3.379'}
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{'loss': '0.2604', 'grad_norm': '3.629', 'learning_rate': '3.412e-06', 'epoch': '3.386'}
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{'loss': '0.246', 'grad_norm': '5.426', 'learning_rate': '3.376e-06', 'epoch': '3.392'}
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{'loss': '0.244', 'grad_norm': '1.848', 'learning_rate': '3.34e-06', 'epoch': '3.399'}
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{'loss': '0.2641', 'grad_norm': '5.125', 'learning_rate': '3.304e-06', 'epoch': '3.405'}
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{'loss': '0.2573', 'grad_norm': '3.377', 'learning_rate': '3.268e-06', 'epoch': '3.412'}
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{'loss': '0.2613', 'grad_norm': '2.842', 'learning_rate': '3.232e-06', 'epoch': '3.418'}
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{'loss': '0.2746', 'grad_norm': '3.197', 'learning_rate': '3.196e-06', 'epoch': '3.425'}
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{'loss': '0.2578', 'grad_norm': '2.158', 'learning_rate': '3.16e-06', 'epoch': '3.431'}
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{'loss': '0.2445', 'grad_norm': '2.634', 'learning_rate': '3.124e-06', 'epoch': '3.438'}
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{'loss': '0.253', 'grad_norm': '2.139', 'learning_rate': '3.088e-06', 'epoch': '3.444'}
|
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+
{'loss': '0.2644', 'grad_norm': '3.682', 'learning_rate': '3.052e-06', 'epoch': '3.451'}
|
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+
{'loss': '0.2656', 'grad_norm': '4.607', 'learning_rate': '3.016e-06', 'epoch': '3.457'}
|
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{'loss': '0.252', 'grad_norm': '2.139', 'learning_rate': '2.98e-06', 'epoch': '3.464'}
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{'loss': '0.2527', 'grad_norm': '2.422', 'learning_rate': '2.944e-06', 'epoch': '3.47'}
|
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{'loss': '0.2534', 'grad_norm': '3.598', 'learning_rate': '2.908e-06', 'epoch': '3.477'}
|
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{'loss': '0.253', 'grad_norm': '1.388', 'learning_rate': '2.872e-06', 'epoch': '3.483'}
|
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{'loss': '0.2614', 'grad_norm': '3.057', 'learning_rate': '2.837e-06', 'epoch': '3.489'}
|
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{'loss': '0.2517', 'grad_norm': '2.296', 'learning_rate': '2.801e-06', 'epoch': '3.496'}
|
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2026-03-04 16:57:03 - TripletEvaluator: Evaluating the model on the dev-768 dataset in epoch 3.4959456178684145 after 108000 steps (truncated to 768):
|
| 345 |
+
2026-03-04 17:20:27 - Accuracy Cosine Similarity: 98.49%
|
| 346 |
+
2026-03-04 17:20:27 - TripletEvaluator: Evaluating the model on the dev-512 dataset in epoch 3.4959456178684145 after 108000 steps (truncated to 512):
|
| 347 |
+
2026-03-04 17:42:09 - Accuracy Cosine Similarity: 98.49%
|
| 348 |
+
2026-03-04 17:42:09 - TripletEvaluator: Evaluating the model on the dev-256 dataset in epoch 3.4959456178684145 after 108000 steps (truncated to 256):
|
| 349 |
+
2026-03-04 18:03:04 - Accuracy Cosine Similarity: 98.51%
|
| 350 |
+
2026-03-04 18:03:04 - TripletEvaluator: Evaluating the model on the dev-128 dataset in epoch 3.4959456178684145 after 108000 steps (truncated to 128):
|
| 351 |
+
2026-03-04 18:23:36 - Accuracy Cosine Similarity: 98.49%
|
| 352 |
+
2026-03-04 18:23:36 - TripletEvaluator: Evaluating the model on the dev-64 dataset in epoch 3.4959456178684145 after 108000 steps (truncated to 64):
|
| 353 |
+
2026-03-04 18:44:21 - Accuracy Cosine Similarity: 98.41%
|
| 354 |
+
{'eval_train_loss': '0.2252', 'eval_dev-768_cosine_accuracy': '0.9849', 'eval_dev-512_cosine_accuracy': '0.9849', 'eval_dev-256_cosine_accuracy': '0.9851', 'eval_dev-128_cosine_accuracy': '0.9849', 'eval_dev-64_cosine_accuracy': '0.9841', 'eval_sequential_score': '0.9849', 'eval_train_runtime': '9737', 'eval_train_samples_per_second': '116', 'eval_train_steps_per_second': '14.5', 'epoch': '3.496'}
|
| 355 |
+
2026-03-04 18:44:21 - Saving model checkpoint to /home/skiredj.abderrahman/khalil/sbert_training/output/arabert_20260224_1730/checkpoint-108000
|
| 356 |
+
2026-03-04 18:44:21 - Save model to /home/skiredj.abderrahman/khalil/sbert_training/output/arabert_20260224_1730/checkpoint-108000
|
| 357 |
+
{'loss': '0.2589', 'grad_norm': '2.231', 'learning_rate': '2.765e-06', 'epoch': '3.502'}
|
| 358 |
+
{'loss': '0.2582', 'grad_norm': '5.077', 'learning_rate': '2.729e-06', 'epoch': '3.509'}
|
| 359 |
+
{'loss': '0.2553', 'grad_norm': '4.234', 'learning_rate': '2.693e-06', 'epoch': '3.515'}
|
| 360 |
+
{'loss': '0.2457', 'grad_norm': '2.167', 'learning_rate': '2.657e-06', 'epoch': '3.522'}
|
| 361 |
+
{'loss': '0.2662', 'grad_norm': '4.357', 'learning_rate': '2.621e-06', 'epoch': '3.528'}
|
| 362 |
+
{'loss': '0.2619', 'grad_norm': '3.987', 'learning_rate': '2.585e-06', 'epoch': '3.535'}
|
| 363 |
+
{'loss': '0.2556', 'grad_norm': '1.6', 'learning_rate': '2.549e-06', 'epoch': '3.541'}
|
| 364 |
+
{'loss': '0.2635', 'grad_norm': '2.069', 'learning_rate': '2.513e-06', 'epoch': '3.548'}
|
| 365 |
+
{'loss': '0.255', 'grad_norm': '2.314', 'learning_rate': '2.477e-06', 'epoch': '3.554'}
|
| 366 |
+
{'loss': '0.267', 'grad_norm': '4.255', 'learning_rate': '2.441e-06', 'epoch': '3.561'}
|
| 367 |
+
{'loss': '0.266', 'grad_norm': '3.208', 'learning_rate': '2.405e-06', 'epoch': '3.567'}
|
| 368 |
+
{'loss': '0.2604', 'grad_norm': '3.669', 'learning_rate': '2.369e-06', 'epoch': '3.574'}
|
| 369 |
+
{'loss': '0.2574', 'grad_norm': '3.103', 'learning_rate': '2.333e-06', 'epoch': '3.58'}
|
| 370 |
+
{'loss': '0.2607', 'grad_norm': '4.632', 'learning_rate': '2.297e-06', 'epoch': '3.587'}
|
| 371 |
+
{'loss': '0.2465', 'grad_norm': '4', 'learning_rate': '2.261e-06', 'epoch': '3.593'}
|
| 372 |
+
{'loss': '0.279', 'grad_norm': '3.647', 'learning_rate': '2.225e-06', 'epoch': '3.6'}
|
| 373 |
+
{'loss': '0.2681', 'grad_norm': '5.511', 'learning_rate': '2.189e-06', 'epoch': '3.606'}
|
| 374 |
+
{'loss': '0.2654', 'grad_norm': '1.217', 'learning_rate': '2.153e-06', 'epoch': '3.612'}
|
| 375 |
+
{'loss': '0.264', 'grad_norm': '4.641', 'learning_rate': '2.117e-06', 'epoch': '3.619'}
|
| 376 |
+
{'loss': '0.2774', 'grad_norm': '3.571', 'learning_rate': '2.081e-06', 'epoch': '3.625'}
|
| 377 |
+
{'loss': '0.2568', 'grad_norm': '3.808', 'learning_rate': '2.045e-06', 'epoch': '3.632'}
|
| 378 |
+
{'loss': '0.2665', 'grad_norm': '3.178', 'learning_rate': '2.009e-06', 'epoch': '3.638'}
|
| 379 |
+
{'loss': '0.2532', 'grad_norm': '1.566', 'learning_rate': '1.973e-06', 'epoch': '3.645'}
|
| 380 |
+
{'loss': '0.2613', 'grad_norm': '3.262', 'learning_rate': '1.937e-06', 'epoch': '3.651'}
|
| 381 |
+
{'loss': '0.2413', 'grad_norm': '4.082', 'learning_rate': '1.901e-06', 'epoch': '3.658'}
|
| 382 |
+
{'loss': '0.2788', 'grad_norm': '4.851', 'learning_rate': '1.865e-06', 'epoch': '3.664'}
|
| 383 |
+
{'loss': '0.2586', 'grad_norm': '5.756', 'learning_rate': '1.829e-06', 'epoch': '3.671'}
|
| 384 |
+
{'loss': '0.2602', 'grad_norm': '5.288', 'learning_rate': '1.793e-06', 'epoch': '3.677'}
|
| 385 |
+
{'loss': '0.2708', 'grad_norm': '3.492', 'learning_rate': '1.758e-06', 'epoch': '3.684'}
|
| 386 |
+
{'loss': '0.2556', 'grad_norm': '2.688', 'learning_rate': '1.722e-06', 'epoch': '3.69'}
|
| 387 |
+
2026-03-04 20:34:25 - TripletEvaluator: Evaluating the model on the dev-768 dataset in epoch 3.6901675163874725 after 114000 steps (truncated to 768):
|
| 388 |
+
2026-03-04 20:55:09 - Accuracy Cosine Similarity: 98.51%
|
| 389 |
+
2026-03-04 20:55:09 - TripletEvaluator: Evaluating the model on the dev-512 dataset in epoch 3.6901675163874725 after 114000 steps (truncated to 512):
|
| 390 |
+
2026-03-04 21:15:51 - Accuracy Cosine Similarity: 98.52%
|
| 391 |
+
2026-03-04 21:15:51 - TripletEvaluator: Evaluating the model on the dev-256 dataset in epoch 3.6901675163874725 after 114000 steps (truncated to 256):
|
| 392 |
+
2026-03-04 21:36:36 - Accuracy Cosine Similarity: 98.54%
|
| 393 |
+
2026-03-04 21:36:36 - TripletEvaluator: Evaluating the model on the dev-128 dataset in epoch 3.6901675163874725 after 114000 steps (truncated to 128):
|
| 394 |
+
2026-03-04 21:57:23 - Accuracy Cosine Similarity: 98.53%
|
| 395 |
+
2026-03-04 21:57:23 - TripletEvaluator: Evaluating the model on the dev-64 dataset in epoch 3.6901675163874725 after 114000 steps (truncated to 64):
|
| 396 |
+
2026-03-04 22:18:08 - Accuracy Cosine Similarity: 98.44%
|
| 397 |
+
{'eval_train_loss': '0.2188', 'eval_dev-768_cosine_accuracy': '0.9851', 'eval_dev-512_cosine_accuracy': '0.9852', 'eval_dev-256_cosine_accuracy': '0.9854', 'eval_dev-128_cosine_accuracy': '0.9853', 'eval_dev-64_cosine_accuracy': '0.9844', 'eval_sequential_score': '0.9851', 'eval_train_runtime': '9310', 'eval_train_samples_per_second': '121.3', 'eval_train_steps_per_second': '15.17', 'epoch': '3.69'}
|
| 398 |
+
2026-03-04 22:18:08 - Saving model checkpoint to /home/skiredj.abderrahman/khalil/sbert_training/output/arabert_20260224_1730/checkpoint-114000
|
| 399 |
+
2026-03-04 22:18:08 - Save model to /home/skiredj.abderrahman/khalil/sbert_training/output/arabert_20260224_1730/checkpoint-114000
|
| 400 |
+
{'loss': '0.2576', 'grad_norm': '2.428', 'learning_rate': '1.686e-06', 'epoch': '3.697'}
|
| 401 |
+
{'loss': '0.2713', 'grad_norm': '2.93', 'learning_rate': '1.65e-06', 'epoch': '3.703'}
|
| 402 |
+
{'loss': '0.2748', 'grad_norm': '3.659', 'learning_rate': '1.614e-06', 'epoch': '3.71'}
|
| 403 |
+
{'loss': '0.2542', 'grad_norm': '3.45', 'learning_rate': '1.578e-06', 'epoch': '3.716'}
|
| 404 |
+
{'loss': '0.2647', 'grad_norm': '4.824', 'learning_rate': '1.542e-06', 'epoch': '3.723'}
|
| 405 |
+
{'loss': '0.2751', 'grad_norm': '1.834', 'learning_rate': '1.506e-06', 'epoch': '3.729'}
|
| 406 |
+
{'loss': '0.2534', 'grad_norm': '4.17', 'learning_rate': '1.47e-06', 'epoch': '3.735'}
|
| 407 |
+
{'loss': '0.2577', 'grad_norm': '3.893', 'learning_rate': '1.434e-06', 'epoch': '3.742'}
|
| 408 |
+
{'loss': '0.2722', 'grad_norm': '4.691', 'learning_rate': '1.398e-06', 'epoch': '3.748'}
|
| 409 |
+
{'loss': '0.2717', 'grad_norm': '3.968', 'learning_rate': '1.362e-06', 'epoch': '3.755'}
|
| 410 |
+
{'loss': '0.2737', 'grad_norm': '4.474', 'learning_rate': '1.326e-06', 'epoch': '3.761'}
|
| 411 |
+
{'loss': '0.2725', 'grad_norm': '3.462', 'learning_rate': '1.29e-06', 'epoch': '3.768'}
|
| 412 |
+
{'loss': '0.2587', 'grad_norm': '1.857', 'learning_rate': '1.254e-06', 'epoch': '3.774'}
|
| 413 |
+
{'loss': '0.2623', 'grad_norm': '2.164', 'learning_rate': '1.218e-06', 'epoch': '3.781'}
|
| 414 |
+
{'loss': '0.2659', 'grad_norm': '2.674', 'learning_rate': '1.182e-06', 'epoch': '3.787'}
|
| 415 |
+
{'loss': '0.2735', 'grad_norm': '1.56', 'learning_rate': '1.146e-06', 'epoch': '3.794'}
|
| 416 |
+
{'loss': '0.2847', 'grad_norm': '4.402', 'learning_rate': '1.11e-06', 'epoch': '3.8'}
|
| 417 |
+
{'loss': '0.2636', 'grad_norm': '5.62', 'learning_rate': '1.074e-06', 'epoch': '3.807'}
|
| 418 |
+
{'loss': '0.2777', 'grad_norm': '4.51', 'learning_rate': '1.038e-06', 'epoch': '3.813'}
|
| 419 |
+
{'loss': '0.2751', 'grad_norm': '3.339', 'learning_rate': '1.002e-06', 'epoch': '3.82'}
|
| 420 |
+
{'loss': '0.2609', 'grad_norm': '3.817', 'learning_rate': '9.662e-07', 'epoch': '3.826'}
|
| 421 |
+
{'loss': '0.2684', 'grad_norm': '4.592', 'learning_rate': '9.303e-07', 'epoch': '3.833'}
|
| 422 |
+
{'loss': '0.2772', 'grad_norm': '2.499', 'learning_rate': '8.943e-07', 'epoch': '3.839'}
|
| 423 |
+
{'loss': '0.2684', 'grad_norm': '4.28', 'learning_rate': '8.583e-07', 'epoch': '3.846'}
|
| 424 |
+
{'loss': '0.2682', 'grad_norm': '3.179', 'learning_rate': '8.224e-07', 'epoch': '3.852'}
|
| 425 |
+
{'loss': '0.2784', 'grad_norm': '5.42', 'learning_rate': '7.864e-07', 'epoch': '3.858'}
|
| 426 |
+
{'loss': '0.2735', 'grad_norm': '2.668', 'learning_rate': '7.504e-07', 'epoch': '3.865'}
|
| 427 |
+
{'loss': '0.2848', 'grad_norm': '4.855', 'learning_rate': '7.145e-07', 'epoch': '3.871'}
|
| 428 |
+
{'loss': '0.2638', 'grad_norm': '2.601', 'learning_rate': '6.785e-07', 'epoch': '3.878'}
|
| 429 |
+
{'loss': '0.2711', 'grad_norm': '4.021', 'learning_rate': '6.425e-07', 'epoch': '3.884'}
|
| 430 |
+
2026-03-05 00:09:04 - TripletEvaluator: Evaluating the model on the dev-768 dataset in epoch 3.8843894149065306 after 120000 steps (truncated to 768):
|
| 431 |
+
2026-03-05 00:30:13 - Accuracy Cosine Similarity: 98.54%
|
| 432 |
+
2026-03-05 00:30:13 - TripletEvaluator: Evaluating the model on the dev-512 dataset in epoch 3.8843894149065306 after 120000 steps (truncated to 512):
|
| 433 |
+
2026-03-05 00:51:03 - Accuracy Cosine Similarity: 98.55%
|
| 434 |
+
2026-03-05 00:51:03 - TripletEvaluator: Evaluating the model on the dev-256 dataset in epoch 3.8843894149065306 after 120000 steps (truncated to 256):
|
| 435 |
+
2026-03-05 01:11:59 - Accuracy Cosine Similarity: 98.56%
|
| 436 |
+
2026-03-05 01:11:59 - TripletEvaluator: Evaluating the model on the dev-128 dataset in epoch 3.8843894149065306 after 120000 steps (truncated to 128):
|
| 437 |
+
2026-03-05 01:32:50 - Accuracy Cosine Similarity: 98.55%
|
| 438 |
+
2026-03-05 01:32:50 - TripletEvaluator: Evaluating the model on the dev-64 dataset in epoch 3.8843894149065306 after 120000 steps (truncated to 64):
|
| 439 |
+
2026-03-05 01:53:42 - Accuracy Cosine Similarity: 98.46%
|
| 440 |
+
{'eval_train_loss': '0.2171', 'eval_dev-768_cosine_accuracy': '0.9854', 'eval_dev-512_cosine_accuracy': '0.9855', 'eval_dev-256_cosine_accuracy': '0.9856', 'eval_dev-128_cosine_accuracy': '0.9855', 'eval_dev-64_cosine_accuracy': '0.9846', 'eval_sequential_score': '0.9854', 'eval_train_runtime': '9383', 'eval_train_samples_per_second': '120.4', 'eval_train_steps_per_second': '15.05', 'epoch': '3.884'}
|
| 441 |
+
2026-03-05 01:53:42 - Saving model checkpoint to /home/skiredj.abderrahman/khalil/sbert_training/output/arabert_20260224_1730/checkpoint-120000
|
| 442 |
+
2026-03-05 01:53:42 - Save model to /home/skiredj.abderrahman/khalil/sbert_training/output/arabert_20260224_1730/checkpoint-120000
|
| 443 |
+
{'loss': '0.2825', 'grad_norm': '2.434', 'learning_rate': '6.066e-07', 'epoch': '3.891'}
|
| 444 |
+
{'loss': '0.2724', 'grad_norm': '3.687', 'learning_rate': '5.706e-07', 'epoch': '3.897'}
|
| 445 |
+
{'loss': '0.3078', 'grad_norm': '5.537', 'learning_rate': '5.346e-07', 'epoch': '3.904'}
|
| 446 |
+
{'loss': '0.2806', 'grad_norm': '3.519', 'learning_rate': '4.987e-07', 'epoch': '3.91'}
|
| 447 |
+
{'loss': '0.2631', 'grad_norm': '3.982', 'learning_rate': '4.627e-07', 'epoch': '3.917'}
|
| 448 |
+
{'loss': '0.2892', 'grad_norm': '3.621', 'learning_rate': '4.267e-07', 'epoch': '3.923'}
|
| 449 |
+
{'loss': '0.2791', 'grad_norm': '4.467', 'learning_rate': '3.908e-07', 'epoch': '3.93'}
|
| 450 |
+
{'loss': '0.2874', 'grad_norm': '4.958', 'learning_rate': '3.548e-07', 'epoch': '3.936'}
|
| 451 |
+
{'loss': '0.2602', 'grad_norm': '3.852', 'learning_rate': '3.188e-07', 'epoch': '3.943'}
|
| 452 |
+
{'loss': '0.2988', 'grad_norm': '2.639', 'learning_rate': '2.829e-07', 'epoch': '3.949'}
|
| 453 |
+
{'loss': '0.2935', 'grad_norm': '1.878', 'learning_rate': '2.469e-07', 'epoch': '3.956'}
|
| 454 |
+
{'loss': '0.2999', 'grad_norm': '3.6', 'learning_rate': '2.109e-07', 'epoch': '3.962'}
|
| 455 |
+
{'loss': '0.293', 'grad_norm': '2.28', 'learning_rate': '1.75e-07', 'epoch': '3.969'}
|
| 456 |
+
{'loss': '0.2784', 'grad_norm': '4.347', 'learning_rate': '1.39e-07', 'epoch': '3.975'}
|
| 457 |
+
{'loss': '0.3013', 'grad_norm': '5.014', 'learning_rate': '1.03e-07', 'epoch': '3.982'}
|
| 458 |
+
{'loss': '0.2919', 'grad_norm': '2.171', 'learning_rate': '6.708e-08', 'epoch': '3.988'}
|
| 459 |
+
{'loss': '0.3011', 'grad_norm': '5.241', 'learning_rate': '3.111e-08', 'epoch': '3.994'}
|
| 460 |
+
2026-03-05 02:28:54 - Saving model checkpoint to /home/skiredj.abderrahman/khalil/sbert_training/output/arabert_20260224_1730/checkpoint-123572
|
| 461 |
+
2026-03-05 02:28:54 - Save model to /home/skiredj.abderrahman/khalil/sbert_training/output/arabert_20260224_1730/checkpoint-123572
|
| 462 |
+
{'train_runtime': '4.115e+04', 'train_samples_per_second': '384.4', 'train_steps_per_second': '3.003', 'train_loss': '0.04643', 'epoch': '4'}
|
| 463 |
+
2026-03-05 02:28:56 - Save model to /home/skiredj.abderrahman/khalil/sbert_training/output/final_epoch4
|
| 464 |
+
model saved successfully
|
| 465 |
+
2026-03-05 02:28:56 - TripletEvaluator: Evaluating the model on the test-768 dataset (truncated to 768):
|
| 466 |
+
2026-03-05 02:47:53 - Accuracy Cosine Similarity: 98.54%
|
| 467 |
+
2026-03-05 02:47:53 - TripletEvaluator: Evaluating the model on the test-512 dataset (truncated to 512):
|
| 468 |
+
2026-03-05 03:06:39 - Accuracy Cosine Similarity: 98.56%
|
| 469 |
+
2026-03-05 03:06:39 - TripletEvaluator: Evaluating the model on the test-256 dataset (truncated to 256):
|
| 470 |
+
2026-03-05 03:25:22 - Accuracy Cosine Similarity: 98.57%
|
| 471 |
+
2026-03-05 03:25:22 - TripletEvaluator: Evaluating the model on the test-128 dataset (truncated to 128):
|
| 472 |
+
2026-03-05 03:44:01 - Accuracy Cosine Similarity: 98.55%
|
| 473 |
+
2026-03-05 03:44:01 - TripletEvaluator: Evaluating the model on the test-64 dataset (truncated to 64):
|
| 474 |
+
2026-03-05 04:02:46 - Accuracy Cosine Similarity: 98.46%
|
epoch4/model/1_Pooling/config.json
ADDED
|
@@ -0,0 +1,10 @@
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+
{
|
| 2 |
+
"word_embedding_dimension": 768,
|
| 3 |
+
"pooling_mode_cls_token": false,
|
| 4 |
+
"pooling_mode_mean_tokens": true,
|
| 5 |
+
"pooling_mode_max_tokens": false,
|
| 6 |
+
"pooling_mode_mean_sqrt_len_tokens": false,
|
| 7 |
+
"pooling_mode_weightedmean_tokens": false,
|
| 8 |
+
"pooling_mode_lasttoken": false,
|
| 9 |
+
"include_prompt": true
|
| 10 |
+
}
|
epoch4/model/README.md
ADDED
|
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|
| 1 |
+
---
|
| 2 |
+
tags:
|
| 3 |
+
- sentence-transformers
|
| 4 |
+
- sentence-similarity
|
| 5 |
+
- feature-extraction
|
| 6 |
+
- dense
|
| 7 |
+
- generated_from_trainer
|
| 8 |
+
- dataset_size:3954179
|
| 9 |
+
- loss:MatryoshkaLoss
|
| 10 |
+
- loss:MultipleNegativesRankingLoss
|
| 11 |
+
widget:
|
| 12 |
+
- source_sentence: إذا لم تكن قد فعلت ذلك بالفعل ، تحقق من تصميمنا الجديد للمراسلات
|
| 13 |
+
والحوارات واليوميات .
|
| 14 |
+
sentences:
|
| 15 |
+
- تم إعادة تصميم الرسائل والحوارات .
|
| 16 |
+
- يقدم مقهى بارج كل من وجبات الغداء والإفطار .
|
| 17 |
+
- قبل ان نعرف اسماء بعضنا او اي شيء قد تعانقنا وبكىنا .
|
| 18 |
+
- source_sentence: أين تقع جامعة واينزبرج
|
| 19 |
+
sentences:
|
| 20 |
+
- جامعة دالاس بابتيست ( DBU ) ، المعروفة سابقا باسم كلية دالاس بابتيست ، هي جامعة
|
| 21 |
+
فنون ليبرالية مسيحية تقع في دالاس ، تكساس . يقع الحرم الجامعي الرئيسي على بعد
|
| 22 |
+
حوالي 12 ميلا ( 19 كم ) جنوب غرب وسط مدينة دالاس ويطل على بحيرة ماونتين كريك .
|
| 23 |
+
تأسست جامعة دالاس بابتيست عام 1898 باسم كلية ديكاتور بابتيست ، وتدير حاليا حرما
|
| 24 |
+
جامعيا في دالاس وبلانو وهيرست .
|
| 25 |
+
- الزوجان معا
|
| 26 |
+
- تقع جامعة واينسبرغ في حرم جامعي معاصر في تلال جنوب غرب ولاية بنسلفانيا ، مع ثلاثة
|
| 27 |
+
مراكز للبالغين تقع في مناطق بيتسبرغ في ساوثبوينت وكرانبيري ومونروفيل . تم إدراج
|
| 28 |
+
Hanna Hall و Miller Hall في السجل الوطني للأماكن التاريخية .
|
| 29 |
+
- source_sentence: The isolated Russian forces resisted in several areas for two more
|
| 30 |
+
days .
|
| 31 |
+
sentences:
|
| 32 |
+
- 'ياهو : كيف يمكنني معرفة ما إذا كان البريد الإلكتروني الذي أرسلته قد تم استلامه
|
| 33 |
+
أو قراءته ؟'
|
| 34 |
+
- واستمرت الاشتباكات الحدودية خلال اليومين المقبلين ، حيث استهدفت المخافر الحدودية
|
| 35 |
+
من الجانبين والتي أسفرت عن وقوع عشرات الإصابات .
|
| 36 |
+
- قاومت القوات الروسية المعزولة في عة مناطق لمدة يومين آخرين .
|
| 37 |
+
- source_sentence: فتاة هيبي بشعر أشقر وأرجواني على الجانب يرتدي قميص أبيض وملابس
|
| 38 |
+
سوداء
|
| 39 |
+
sentences:
|
| 40 |
+
- فتاة " هيبي " ترتدي قميصا أبيضا وملابس سوداء شعرها أشقر وأحمر
|
| 41 |
+
- المرأة تضع يدها في جيب الرجل
|
| 42 |
+
- فتاة لديها سترة حمراء وسوداء
|
| 43 |
+
- source_sentence: رجل وامرأة يجلسان في سيارة ووجههما في الاتجاه المعاكس من الكاميرا
|
| 44 |
+
sentences:
|
| 45 |
+
- هناك شخصان وسيارة
|
| 46 |
+
- سيارة صدئة هي الشيء الوحيد المرئي
|
| 47 |
+
- كان أفضل حالا
|
| 48 |
+
pipeline_tag: sentence-similarity
|
| 49 |
+
library_name: sentence-transformers
|
| 50 |
+
metrics:
|
| 51 |
+
- cosine_accuracy
|
| 52 |
+
model-index:
|
| 53 |
+
- name: SentenceTransformer
|
| 54 |
+
results:
|
| 55 |
+
- task:
|
| 56 |
+
type: triplet
|
| 57 |
+
name: Triplet
|
| 58 |
+
dataset:
|
| 59 |
+
name: dev 768
|
| 60 |
+
type: dev-768
|
| 61 |
+
metrics:
|
| 62 |
+
- type: cosine_accuracy
|
| 63 |
+
value: 0.9853799939155579
|
| 64 |
+
name: Cosine Accuracy
|
| 65 |
+
- task:
|
| 66 |
+
type: triplet
|
| 67 |
+
name: Triplet
|
| 68 |
+
dataset:
|
| 69 |
+
name: dev 512
|
| 70 |
+
type: dev-512
|
| 71 |
+
metrics:
|
| 72 |
+
- type: cosine_accuracy
|
| 73 |
+
value: 0.9855160117149353
|
| 74 |
+
name: Cosine Accuracy
|
| 75 |
+
- task:
|
| 76 |
+
type: triplet
|
| 77 |
+
name: Triplet
|
| 78 |
+
dataset:
|
| 79 |
+
name: dev 256
|
| 80 |
+
type: dev-256
|
| 81 |
+
metrics:
|
| 82 |
+
- type: cosine_accuracy
|
| 83 |
+
value: 0.985588014125824
|
| 84 |
+
name: Cosine Accuracy
|
| 85 |
+
- task:
|
| 86 |
+
type: triplet
|
| 87 |
+
name: Triplet
|
| 88 |
+
dataset:
|
| 89 |
+
name: dev 128
|
| 90 |
+
type: dev-128
|
| 91 |
+
metrics:
|
| 92 |
+
- type: cosine_accuracy
|
| 93 |
+
value: 0.9855039715766907
|
| 94 |
+
name: Cosine Accuracy
|
| 95 |
+
- task:
|
| 96 |
+
type: triplet
|
| 97 |
+
name: Triplet
|
| 98 |
+
dataset:
|
| 99 |
+
name: dev 64
|
| 100 |
+
type: dev-64
|
| 101 |
+
metrics:
|
| 102 |
+
- type: cosine_accuracy
|
| 103 |
+
value: 0.9845880270004272
|
| 104 |
+
name: Cosine Accuracy
|
| 105 |
+
---
|
| 106 |
+
|
| 107 |
+
# SentenceTransformer
|
| 108 |
+
|
| 109 |
+
This is a [sentence-transformers](https://www.SBERT.net) model trained on the train dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
|
| 110 |
+
|
| 111 |
+
## Model Details
|
| 112 |
+
|
| 113 |
+
### Model Description
|
| 114 |
+
- **Model Type:** Sentence Transformer
|
| 115 |
+
<!-- - **Base model:** [Unknown](https://huggingface.co/unknown) -->
|
| 116 |
+
- **Maximum Sequence Length:** 512 tokens
|
| 117 |
+
- **Output Dimensionality:** 768 dimensions
|
| 118 |
+
- **Similarity Function:** Cosine Similarity
|
| 119 |
+
- **Training Dataset:**
|
| 120 |
+
- train
|
| 121 |
+
<!-- - **Language:** Unknown -->
|
| 122 |
+
<!-- - **License:** Unknown -->
|
| 123 |
+
|
| 124 |
+
### Model Sources
|
| 125 |
+
|
| 126 |
+
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
|
| 127 |
+
- **Repository:** [Sentence Transformers on GitHub](https://github.com/huggingface/sentence-transformers)
|
| 128 |
+
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
|
| 129 |
+
|
| 130 |
+
### Full Model Architecture
|
| 131 |
+
|
| 132 |
+
```
|
| 133 |
+
SentenceTransformer(
|
| 134 |
+
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False, 'architecture': 'BertModel'})
|
| 135 |
+
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
|
| 136 |
+
)
|
| 137 |
+
```
|
| 138 |
+
|
| 139 |
+
## Usage
|
| 140 |
+
|
| 141 |
+
### Direct Usage (Sentence Transformers)
|
| 142 |
+
|
| 143 |
+
First install the Sentence Transformers library:
|
| 144 |
+
|
| 145 |
+
```bash
|
| 146 |
+
pip install -U sentence-transformers
|
| 147 |
+
```
|
| 148 |
+
|
| 149 |
+
Then you can load this model and run inference.
|
| 150 |
+
```python
|
| 151 |
+
from sentence_transformers import SentenceTransformer
|
| 152 |
+
|
| 153 |
+
# Download from the 🤗 Hub
|
| 154 |
+
model = SentenceTransformer("sentence_transformers_model_id")
|
| 155 |
+
# Run inference
|
| 156 |
+
sentences = [
|
| 157 |
+
'رجل وامرأة يجلسان في سيارة ووجههما في الاتجاه المعاكس من الكاميرا',
|
| 158 |
+
'هناك شخصان وسيارة',
|
| 159 |
+
'سيارة صدئة هي الشيء الوحيد المرئي',
|
| 160 |
+
]
|
| 161 |
+
embeddings = model.encode(sentences)
|
| 162 |
+
print(embeddings.shape)
|
| 163 |
+
# [3, 768]
|
| 164 |
+
|
| 165 |
+
# Get the similarity scores for the embeddings
|
| 166 |
+
similarities = model.similarity(embeddings, embeddings)
|
| 167 |
+
print(similarities)
|
| 168 |
+
# tensor([[1.0000, 0.6553, 0.2590],
|
| 169 |
+
# [0.6553, 1.0000, 0.3695],
|
| 170 |
+
# [0.2590, 0.3695, 1.0000]])
|
| 171 |
+
```
|
| 172 |
+
|
| 173 |
+
<!--
|
| 174 |
+
### Direct Usage (Transformers)
|
| 175 |
+
|
| 176 |
+
<details><summary>Click to see the direct usage in Transformers</summary>
|
| 177 |
+
|
| 178 |
+
</details>
|
| 179 |
+
-->
|
| 180 |
+
|
| 181 |
+
<!--
|
| 182 |
+
### Downstream Usage (Sentence Transformers)
|
| 183 |
+
|
| 184 |
+
You can finetune this model on your own dataset.
|
| 185 |
+
|
| 186 |
+
<details><summary>Click to expand</summary>
|
| 187 |
+
|
| 188 |
+
</details>
|
| 189 |
+
-->
|
| 190 |
+
|
| 191 |
+
<!--
|
| 192 |
+
### Out-of-Scope Use
|
| 193 |
+
|
| 194 |
+
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
|
| 195 |
+
-->
|
| 196 |
+
|
| 197 |
+
## Evaluation
|
| 198 |
+
|
| 199 |
+
### Metrics
|
| 200 |
+
|
| 201 |
+
#### Triplet
|
| 202 |
+
|
| 203 |
+
* Dataset: `dev-768`
|
| 204 |
+
* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator) with these parameters:
|
| 205 |
+
```json
|
| 206 |
+
{
|
| 207 |
+
"truncate_dim": 768
|
| 208 |
+
}
|
| 209 |
+
```
|
| 210 |
+
|
| 211 |
+
| Metric | Value |
|
| 212 |
+
|:--------------------|:-----------|
|
| 213 |
+
| **cosine_accuracy** | **0.9854** |
|
| 214 |
+
|
| 215 |
+
#### Triplet
|
| 216 |
+
|
| 217 |
+
* Dataset: `dev-512`
|
| 218 |
+
* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator) with these parameters:
|
| 219 |
+
```json
|
| 220 |
+
{
|
| 221 |
+
"truncate_dim": 512
|
| 222 |
+
}
|
| 223 |
+
```
|
| 224 |
+
|
| 225 |
+
| Metric | Value |
|
| 226 |
+
|:--------------------|:-----------|
|
| 227 |
+
| **cosine_accuracy** | **0.9855** |
|
| 228 |
+
|
| 229 |
+
#### Triplet
|
| 230 |
+
|
| 231 |
+
* Dataset: `dev-256`
|
| 232 |
+
* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator) with these parameters:
|
| 233 |
+
```json
|
| 234 |
+
{
|
| 235 |
+
"truncate_dim": 256
|
| 236 |
+
}
|
| 237 |
+
```
|
| 238 |
+
|
| 239 |
+
| Metric | Value |
|
| 240 |
+
|:--------------------|:-----------|
|
| 241 |
+
| **cosine_accuracy** | **0.9856** |
|
| 242 |
+
|
| 243 |
+
#### Triplet
|
| 244 |
+
|
| 245 |
+
* Dataset: `dev-128`
|
| 246 |
+
* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator) with these parameters:
|
| 247 |
+
```json
|
| 248 |
+
{
|
| 249 |
+
"truncate_dim": 128
|
| 250 |
+
}
|
| 251 |
+
```
|
| 252 |
+
|
| 253 |
+
| Metric | Value |
|
| 254 |
+
|:--------------------|:-----------|
|
| 255 |
+
| **cosine_accuracy** | **0.9855** |
|
| 256 |
+
|
| 257 |
+
#### Triplet
|
| 258 |
+
|
| 259 |
+
* Dataset: `dev-64`
|
| 260 |
+
* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator) with these parameters:
|
| 261 |
+
```json
|
| 262 |
+
{
|
| 263 |
+
"truncate_dim": 64
|
| 264 |
+
}
|
| 265 |
+
```
|
| 266 |
+
|
| 267 |
+
| Metric | Value |
|
| 268 |
+
|:--------------------|:-----------|
|
| 269 |
+
| **cosine_accuracy** | **0.9846** |
|
| 270 |
+
|
| 271 |
+
<!--
|
| 272 |
+
## Bias, Risks and Limitations
|
| 273 |
+
|
| 274 |
+
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
|
| 275 |
+
-->
|
| 276 |
+
|
| 277 |
+
<!--
|
| 278 |
+
### Recommendations
|
| 279 |
+
|
| 280 |
+
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
|
| 281 |
+
-->
|
| 282 |
+
|
| 283 |
+
## Training Details
|
| 284 |
+
|
| 285 |
+
### Training Dataset
|
| 286 |
+
|
| 287 |
+
#### train
|
| 288 |
+
|
| 289 |
+
* Dataset: train
|
| 290 |
+
* Size: 3,954,179 training samples
|
| 291 |
+
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
|
| 292 |
+
* Approximate statistics based on the first 1000 samples:
|
| 293 |
+
| | anchor | positive | negative |
|
| 294 |
+
|:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
|
| 295 |
+
| type | string | string | string |
|
| 296 |
+
| details | <ul><li>min: 4 tokens</li><li>mean: 16.1 tokens</li><li>max: 113 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 41.85 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 41.99 tokens</li><li>max: 512 tokens</li></ul> |
|
| 297 |
+
* Samples:
|
| 298 |
+
| anchor | positive | negative |
|
| 299 |
+
|:----------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
|
| 300 |
+
| <code>في أي مقاطعة تقع لويسفيل أركنساس</code> | <code>لويسفيل هي بلدة في مقاطعة لافاييت ، أركنساس ، الولايات المتحدة . كان عدد السكان 1285 في تعداد عام 2000 . . المدينة هي مقر مقاطعة لافاييت .</code> | <code>ماونتن هوم ، أركنساس . ماونتن هوم هي مدينة صغيرة في مقاطعة باكستر ، أركنساس ، الولايات المتحدة ، في جبال أوزارك الجنوبية بالقرب من حدود الولاية الشمالية مع ميسوري . اعتبارا من تعداد عام 2010 ، بلغ عدد سكان المدينة 12448 نسمة .</code> |
|
| 301 |
+
| <code>متوسط سمك باب الخزانة</code> | <code>تتميز أبواب العالم القديم بميزات رائعة مثل السماكة المتزايدة ، والملامح الأعمق ، والأعمدة والقضبان الأوسع لإضفاء مظهر وإحساس أكثر دراماتيكية عند مقارنتها بأبواب الخزانة التقليدية . يبلغ عرض Stiles Rails القياسية 3 بوصات ويمكن تصنيعها في 1 و 1 1 - 8 و 1 سمك .</code> | <code>اعتمادا على الخطأ في اللوحة ، يبلغ متوسط أسعار الإصلاح 130 دولارا لإصلاح الأبواب الفولاذية و 190 دولارا للخشب و 170 دولارا للألمنيوم و 150 دولارا للألياف الزجاجية . مزيد من المعلومات حول كيفية استبدال لوحة باب المرآب . إذا تعطلت أداة فتح باب الجراج ، فقد تكون سلامتك في خطر . تريد التأكد من أن بابك يعمل بشكل صحيح حتى لا يغلق بطريق الخطأ على حيوان أليف أو شخص . تريد أيضا إغلاقها لإبعاد اللصوص عن منزلك .</code> |
|
| 302 |
+
| <code>ما هو تعريف الملء</code> | <code>اعادة تعبئه . اسم تخصيص ثان لوكيل الوصفات الطبية تم الحصول عليه من الصيدلية ، والذي يسمح به فعل الوصفة الأصلية علم الأدوية للحصول على المزيد من دواء معين ، بعد استخدام الكمية الموصوفة في البداية من الوكيل أو إعطائها . انظر الوصفة الطبية .</code> | <code>تعليمات إعادة الملء قم بإعادة الملء فقط باستخدام Spectracide ' Bug Stop Home Barrier Refill . قم بإزالة الغطاء . قم بقياس وصب 12 . 8 أونصة سائلة من المركز في حاوية فارغة سعة 1 جالون من Spectracide - Bug Stop - حاجز منزلي ، واملأه حتى 1 جالون بالماء ، استبدل الغطاء وأغلقه بإحكام . المنتج المنسكب قم بقياس 12 . 8 أونصة سائلة من المركز وصبها بحذر في حاوية فارغة سعة 1 جالون من Spectracide - حاجز منزلي من Spectracide - حاجز منزلي ، واملأه حتى 1 جالون بالماء . استبدل الغطاء وأغلقه بإحكام . امسح أي منتج مسكوب .</code> |
|
| 303 |
+
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
|
| 304 |
+
```json
|
| 305 |
+
{
|
| 306 |
+
"loss": "MultipleNegativesRankingLoss",
|
| 307 |
+
"matryoshka_dims": [
|
| 308 |
+
768,
|
| 309 |
+
512,
|
| 310 |
+
256,
|
| 311 |
+
128,
|
| 312 |
+
64
|
| 313 |
+
],
|
| 314 |
+
"matryoshka_weights": [
|
| 315 |
+
1,
|
| 316 |
+
1,
|
| 317 |
+
1,
|
| 318 |
+
1,
|
| 319 |
+
1
|
| 320 |
+
],
|
| 321 |
+
"n_dims_per_step": -1
|
| 322 |
+
}
|
| 323 |
+
```
|
| 324 |
+
|
| 325 |
+
### Evaluation Dataset
|
| 326 |
+
|
| 327 |
+
#### train
|
| 328 |
+
|
| 329 |
+
* Dataset: train
|
| 330 |
+
* Size: 1,129,759 evaluation samples
|
| 331 |
+
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
|
| 332 |
+
* Approximate statistics based on the first 1000 samples:
|
| 333 |
+
| | anchor | positive | negative |
|
| 334 |
+
|:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
|
| 335 |
+
| type | string | string | string |
|
| 336 |
+
| details | <ul><li>min: 4 tokens</li><li>mean: 16.7 tokens</li><li>max: 150 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 36.54 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 35.42 tokens</li><li>max: 512 tokens</li></ul> |
|
| 337 |
+
* Samples:
|
| 338 |
+
| anchor | positive | negative |
|
| 339 |
+
|:---------------------------------------------------------------------|:---------------------------------|:----------------------------------------------------------------------|
|
| 340 |
+
| <code>رجل يرتدي سروال تنس أزرق وقميص بولو أبيض يضرب كرة التنس</code> | <code>رجل يلعب رياضة</code> | <code>هناك رجل يرتدي زي البيسبول يضرب كرة البيسبول بمضرب التنس</code> |
|
| 341 |
+
| <code>امرأة في ثوب أسود تبدو متفاجئة</code> | <code>امرأة تغيرت مشاعرها</code> | <code>امرأة تسبح في المحيط</code> |
|
| 342 |
+
| <code>رجل يرتدي قميص أبيض يقفز على شيء ما على دراجته الصفراء</code> | <code>رجل يركب دراجته</code> | <code>رجل يركب لوح التزلج فوق المنحدر</code> |
|
| 343 |
+
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
|
| 344 |
+
```json
|
| 345 |
+
{
|
| 346 |
+
"loss": "MultipleNegativesRankingLoss",
|
| 347 |
+
"matryoshka_dims": [
|
| 348 |
+
768,
|
| 349 |
+
512,
|
| 350 |
+
256,
|
| 351 |
+
128,
|
| 352 |
+
64
|
| 353 |
+
],
|
| 354 |
+
"matryoshka_weights": [
|
| 355 |
+
1,
|
| 356 |
+
1,
|
| 357 |
+
1,
|
| 358 |
+
1,
|
| 359 |
+
1
|
| 360 |
+
],
|
| 361 |
+
"n_dims_per_step": -1
|
| 362 |
+
}
|
| 363 |
+
```
|
| 364 |
+
|
| 365 |
+
### Training Hyperparameters
|
| 366 |
+
#### Non-Default Hyperparameters
|
| 367 |
+
|
| 368 |
+
- `per_device_train_batch_size`: 64
|
| 369 |
+
- `num_train_epochs`: 4
|
| 370 |
+
- `learning_rate`: 2e-05
|
| 371 |
+
- `warmup_steps`: 0.1
|
| 372 |
+
- `gradient_accumulation_steps`: 2
|
| 373 |
+
- `bf16`: True
|
| 374 |
+
- `eval_strategy`: steps
|
| 375 |
+
- `warmup_ratio`: 0.1
|
| 376 |
+
- `batch_sampler`: no_duplicates
|
| 377 |
+
|
| 378 |
+
#### All Hyperparameters
|
| 379 |
+
<details><summary>Click to expand</summary>
|
| 380 |
+
|
| 381 |
+
- `per_device_train_batch_size`: 64
|
| 382 |
+
- `num_train_epochs`: 4
|
| 383 |
+
- `max_steps`: -1
|
| 384 |
+
- `learning_rate`: 2e-05
|
| 385 |
+
- `lr_scheduler_type`: linear
|
| 386 |
+
- `lr_scheduler_kwargs`: None
|
| 387 |
+
- `warmup_steps`: 0.1
|
| 388 |
+
- `optim`: adamw_torch
|
| 389 |
+
- `optim_args`: None
|
| 390 |
+
- `weight_decay`: 0.0
|
| 391 |
+
- `adam_beta1`: 0.9
|
| 392 |
+
- `adam_beta2`: 0.999
|
| 393 |
+
- `adam_epsilon`: 1e-08
|
| 394 |
+
- `optim_target_modules`: None
|
| 395 |
+
- `gradient_accumulation_steps`: 2
|
| 396 |
+
- `average_tokens_across_devices`: True
|
| 397 |
+
- `max_grad_norm`: 1.0
|
| 398 |
+
- `label_smoothing_factor`: 0.0
|
| 399 |
+
- `bf16`: True
|
| 400 |
+
- `fp16`: False
|
| 401 |
+
- `bf16_full_eval`: False
|
| 402 |
+
- `fp16_full_eval`: False
|
| 403 |
+
- `tf32`: None
|
| 404 |
+
- `gradient_checkpointing`: False
|
| 405 |
+
- `gradient_checkpointing_kwargs`: None
|
| 406 |
+
- `torch_compile`: False
|
| 407 |
+
- `torch_compile_backend`: None
|
| 408 |
+
- `torch_compile_mode`: None
|
| 409 |
+
- `use_liger_kernel`: False
|
| 410 |
+
- `liger_kernel_config`: None
|
| 411 |
+
- `use_cache`: False
|
| 412 |
+
- `neftune_noise_alpha`: None
|
| 413 |
+
- `torch_empty_cache_steps`: None
|
| 414 |
+
- `auto_find_batch_size`: False
|
| 415 |
+
- `log_on_each_node`: True
|
| 416 |
+
- `logging_nan_inf_filter`: True
|
| 417 |
+
- `include_num_input_tokens_seen`: no
|
| 418 |
+
- `log_level`: passive
|
| 419 |
+
- `log_level_replica`: warning
|
| 420 |
+
- `disable_tqdm`: False
|
| 421 |
+
- `project`: huggingface
|
| 422 |
+
- `trackio_space_id`: trackio
|
| 423 |
+
- `eval_strategy`: steps
|
| 424 |
+
- `per_device_eval_batch_size`: 8
|
| 425 |
+
- `prediction_loss_only`: True
|
| 426 |
+
- `eval_on_start`: False
|
| 427 |
+
- `eval_do_concat_batches`: True
|
| 428 |
+
- `eval_use_gather_object`: False
|
| 429 |
+
- `eval_accumulation_steps`: None
|
| 430 |
+
- `include_for_metrics`: []
|
| 431 |
+
- `batch_eval_metrics`: False
|
| 432 |
+
- `save_only_model`: False
|
| 433 |
+
- `save_on_each_node`: False
|
| 434 |
+
- `enable_jit_checkpoint`: False
|
| 435 |
+
- `push_to_hub`: False
|
| 436 |
+
- `hub_private_repo`: None
|
| 437 |
+
- `hub_model_id`: None
|
| 438 |
+
- `hub_strategy`: every_save
|
| 439 |
+
- `hub_always_push`: False
|
| 440 |
+
- `hub_revision`: None
|
| 441 |
+
- `load_best_model_at_end`: False
|
| 442 |
+
- `ignore_data_skip`: False
|
| 443 |
+
- `restore_callback_states_from_checkpoint`: False
|
| 444 |
+
- `full_determinism`: False
|
| 445 |
+
- `seed`: 42
|
| 446 |
+
- `data_seed`: None
|
| 447 |
+
- `use_cpu`: False
|
| 448 |
+
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
|
| 449 |
+
- `parallelism_config`: None
|
| 450 |
+
- `dataloader_drop_last`: False
|
| 451 |
+
- `dataloader_num_workers`: 0
|
| 452 |
+
- `dataloader_pin_memory`: True
|
| 453 |
+
- `dataloader_persistent_workers`: False
|
| 454 |
+
- `dataloader_prefetch_factor`: None
|
| 455 |
+
- `remove_unused_columns`: True
|
| 456 |
+
- `label_names`: None
|
| 457 |
+
- `train_sampling_strategy`: random
|
| 458 |
+
- `length_column_name`: length
|
| 459 |
+
- `ddp_find_unused_parameters`: None
|
| 460 |
+
- `ddp_bucket_cap_mb`: None
|
| 461 |
+
- `ddp_broadcast_buffers`: False
|
| 462 |
+
- `ddp_backend`: None
|
| 463 |
+
- `ddp_timeout`: 1800
|
| 464 |
+
- `fsdp`: []
|
| 465 |
+
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
|
| 466 |
+
- `deepspeed`: None
|
| 467 |
+
- `debug`: []
|
| 468 |
+
- `skip_memory_metrics`: True
|
| 469 |
+
- `do_predict`: False
|
| 470 |
+
- `resume_from_checkpoint`: None
|
| 471 |
+
- `warmup_ratio`: 0.1
|
| 472 |
+
- `local_rank`: -1
|
| 473 |
+
- `prompts`: None
|
| 474 |
+
- `batch_sampler`: no_duplicates
|
| 475 |
+
- `multi_dataset_batch_sampler`: proportional
|
| 476 |
+
- `router_mapping`: {}
|
| 477 |
+
- `learning_rate_mapping`: {}
|
| 478 |
+
|
| 479 |
+
</details>
|
| 480 |
+
|
| 481 |
+
### Training Logs
|
| 482 |
+
<details><summary>Click to expand</summary>
|
| 483 |
+
|
| 484 |
+
| Epoch | Step | Training Loss | train loss | dev-768_cosine_accuracy | dev-512_cosine_accuracy | dev-256_cosine_accuracy | dev-128_cosine_accuracy | dev-64_cosine_accuracy |
|
| 485 |
+
|:------:|:------:|:-------------:|:----------:|:-----------------------:|:-----------------------:|:-----------------------:|:-----------------------:|:----------------------:|
|
| 486 |
+
| 3.3082 | 102200 | 0.2592 | - | - | - | - | - | - |
|
| 487 |
+
| 3.3147 | 102400 | 0.2543 | - | - | - | - | - | - |
|
| 488 |
+
| 3.3211 | 102600 | 0.2641 | - | - | - | - | - | - |
|
| 489 |
+
| 3.3276 | 102800 | 0.2435 | - | - | - | - | - | - |
|
| 490 |
+
| 3.3341 | 103000 | 0.2510 | - | - | - | - | - | - |
|
| 491 |
+
| 3.3406 | 103200 | 0.2481 | - | - | - | - | - | - |
|
| 492 |
+
| 3.3470 | 103400 | 0.2627 | - | - | - | - | - | - |
|
| 493 |
+
| 3.3535 | 103600 | 0.2480 | - | - | - | - | - | - |
|
| 494 |
+
| 3.3600 | 103800 | 0.2636 | - | - | - | - | - | - |
|
| 495 |
+
| 3.3665 | 104000 | 0.2619 | - | - | - | - | - | - |
|
| 496 |
+
| 3.3729 | 104200 | 0.2423 | - | - | - | - | - | - |
|
| 497 |
+
| 3.3794 | 104400 | 0.2505 | - | - | - | - | - | - |
|
| 498 |
+
| 3.3859 | 104600 | 0.2604 | - | - | - | - | - | - |
|
| 499 |
+
| 3.3924 | 104800 | 0.2460 | - | - | - | - | - | - |
|
| 500 |
+
| 3.3988 | 105000 | 0.2440 | - | - | - | - | - | - |
|
| 501 |
+
| 3.4053 | 105200 | 0.2641 | - | - | - | - | - | - |
|
| 502 |
+
| 3.4118 | 105400 | 0.2573 | - | - | - | - | - | - |
|
| 503 |
+
| 3.4183 | 105600 | 0.2613 | - | - | - | - | - | - |
|
| 504 |
+
| 3.4247 | 105800 | 0.2746 | - | - | - | - | - | - |
|
| 505 |
+
| 3.4312 | 106000 | 0.2578 | - | - | - | - | - | - |
|
| 506 |
+
| 3.4377 | 106200 | 0.2445 | - | - | - | - | - | - |
|
| 507 |
+
| 3.4442 | 106400 | 0.2530 | - | - | - | - | - | - |
|
| 508 |
+
| 3.4506 | 106600 | 0.2644 | - | - | - | - | - | - |
|
| 509 |
+
| 3.4571 | 106800 | 0.2656 | - | - | - | - | - | - |
|
| 510 |
+
| 3.4636 | 107000 | 0.2520 | - | - | - | - | - | - |
|
| 511 |
+
| 3.4700 | 107200 | 0.2527 | - | - | - | - | - | - |
|
| 512 |
+
| 3.4765 | 107400 | 0.2534 | - | - | - | - | - | - |
|
| 513 |
+
| 3.4830 | 107600 | 0.2530 | - | - | - | - | - | - |
|
| 514 |
+
| 3.4895 | 107800 | 0.2614 | - | - | - | - | - | - |
|
| 515 |
+
| 3.4959 | 108000 | 0.2517 | 0.2252 | 0.9849 | 0.9849 | 0.9851 | 0.9849 | 0.9841 |
|
| 516 |
+
| 3.5024 | 108200 | 0.2589 | - | - | - | - | - | - |
|
| 517 |
+
| 3.5089 | 108400 | 0.2582 | - | - | - | - | - | - |
|
| 518 |
+
| 3.5154 | 108600 | 0.2553 | - | - | - | - | - | - |
|
| 519 |
+
| 3.5218 | 108800 | 0.2457 | - | - | - | - | - | - |
|
| 520 |
+
| 3.5283 | 109000 | 0.2662 | - | - | - | - | - | - |
|
| 521 |
+
| 3.5348 | 109200 | 0.2619 | - | - | - | - | - | - |
|
| 522 |
+
| 3.5413 | 109400 | 0.2556 | - | - | - | - | - | - |
|
| 523 |
+
| 3.5477 | 109600 | 0.2635 | - | - | - | - | - | - |
|
| 524 |
+
| 3.5542 | 109800 | 0.2550 | - | - | - | - | - | - |
|
| 525 |
+
| 3.5607 | 110000 | 0.2670 | - | - | - | - | - | - |
|
| 526 |
+
| 3.5672 | 110200 | 0.2660 | - | - | - | - | - | - |
|
| 527 |
+
| 3.5736 | 110400 | 0.2604 | - | - | - | - | - | - |
|
| 528 |
+
| 3.5801 | 110600 | 0.2574 | - | - | - | - | - | - |
|
| 529 |
+
| 3.5866 | 110800 | 0.2607 | - | - | - | - | - | - |
|
| 530 |
+
| 3.5931 | 111000 | 0.2465 | - | - | - | - | - | - |
|
| 531 |
+
| 3.5995 | 111200 | 0.2790 | - | - | - | - | - | - |
|
| 532 |
+
| 3.6060 | 111400 | 0.2681 | - | - | - | - | - | - |
|
| 533 |
+
| 3.6125 | 111600 | 0.2654 | - | - | - | - | - | - |
|
| 534 |
+
| 3.6190 | 111800 | 0.2640 | - | - | - | - | - | - |
|
| 535 |
+
| 3.6254 | 112000 | 0.2774 | - | - | - | - | - | - |
|
| 536 |
+
| 3.6319 | 112200 | 0.2568 | - | - | - | - | - | - |
|
| 537 |
+
| 3.6384 | 112400 | 0.2665 | - | - | - | - | - | - |
|
| 538 |
+
| 3.6448 | 112600 | 0.2532 | - | - | - | - | - | - |
|
| 539 |
+
| 3.6513 | 112800 | 0.2613 | - | - | - | - | - | - |
|
| 540 |
+
| 3.6578 | 113000 | 0.2413 | - | - | - | - | - | - |
|
| 541 |
+
| 3.6643 | 113200 | 0.2788 | - | - | - | - | - | - |
|
| 542 |
+
| 3.6707 | 113400 | 0.2586 | - | - | - | - | - | - |
|
| 543 |
+
| 3.6772 | 113600 | 0.2602 | - | - | - | - | - | - |
|
| 544 |
+
| 3.6837 | 113800 | 0.2708 | - | - | - | - | - | - |
|
| 545 |
+
| 3.6902 | 114000 | 0.2556 | 0.2188 | 0.9851 | 0.9852 | 0.9854 | 0.9853 | 0.9844 |
|
| 546 |
+
| 3.6966 | 114200 | 0.2576 | - | - | - | - | - | - |
|
| 547 |
+
| 3.7031 | 114400 | 0.2713 | - | - | - | - | - | - |
|
| 548 |
+
| 3.7096 | 114600 | 0.2748 | - | - | - | - | - | - |
|
| 549 |
+
| 3.7161 | 114800 | 0.2542 | - | - | - | - | - | - |
|
| 550 |
+
| 3.7225 | 115000 | 0.2647 | - | - | - | - | - | - |
|
| 551 |
+
| 3.7290 | 115200 | 0.2751 | - | - | - | - | - | - |
|
| 552 |
+
| 3.7355 | 115400 | 0.2534 | - | - | - | - | - | - |
|
| 553 |
+
| 3.7420 | 115600 | 0.2577 | - | - | - | - | - | - |
|
| 554 |
+
| 3.7484 | 115800 | 0.2722 | - | - | - | - | - | - |
|
| 555 |
+
| 3.7549 | 116000 | 0.2717 | - | - | - | - | - | - |
|
| 556 |
+
| 3.7614 | 116200 | 0.2737 | - | - | - | - | - | - |
|
| 557 |
+
| 3.7679 | 116400 | 0.2725 | - | - | - | - | - | - |
|
| 558 |
+
| 3.7743 | 116600 | 0.2587 | - | - | - | - | - | - |
|
| 559 |
+
| 3.7808 | 116800 | 0.2623 | - | - | - | - | - | - |
|
| 560 |
+
| 3.7873 | 117000 | 0.2659 | - | - | - | - | - | - |
|
| 561 |
+
| 3.7938 | 117200 | 0.2735 | - | - | - | - | - | - |
|
| 562 |
+
| 3.8002 | 117400 | 0.2847 | - | - | - | - | - | - |
|
| 563 |
+
| 3.8067 | 117600 | 0.2636 | - | - | - | - | - | - |
|
| 564 |
+
| 3.8132 | 117800 | 0.2777 | - | - | - | - | - | - |
|
| 565 |
+
| 3.8196 | 118000 | 0.2751 | - | - | - | - | - | - |
|
| 566 |
+
| 3.8261 | 118200 | 0.2609 | - | - | - | - | - | - |
|
| 567 |
+
| 3.8326 | 118400 | 0.2684 | - | - | - | - | - | - |
|
| 568 |
+
| 3.8391 | 118600 | 0.2772 | - | - | - | - | - | - |
|
| 569 |
+
| 3.8455 | 118800 | 0.2684 | - | - | - | - | - | - |
|
| 570 |
+
| 3.8520 | 119000 | 0.2682 | - | - | - | - | - | - |
|
| 571 |
+
| 3.8585 | 119200 | 0.2784 | - | - | - | - | - | - |
|
| 572 |
+
| 3.8650 | 119400 | 0.2735 | - | - | - | - | - | - |
|
| 573 |
+
| 3.8714 | 119600 | 0.2848 | - | - | - | - | - | - |
|
| 574 |
+
| 3.8779 | 119800 | 0.2638 | - | - | - | - | - | - |
|
| 575 |
+
| 3.8844 | 120000 | 0.2711 | 0.2171 | 0.9854 | 0.9855 | 0.9856 | 0.9855 | 0.9846 |
|
| 576 |
+
| 3.8909 | 120200 | 0.2825 | - | - | - | - | - | - |
|
| 577 |
+
| 3.8973 | 120400 | 0.2724 | - | - | - | - | - | - |
|
| 578 |
+
| 3.9038 | 120600 | 0.3078 | - | - | - | - | - | - |
|
| 579 |
+
| 3.9103 | 120800 | 0.2806 | - | - | - | - | - | - |
|
| 580 |
+
| 3.9168 | 121000 | 0.2631 | - | - | - | - | - | - |
|
| 581 |
+
| 3.9232 | 121200 | 0.2892 | - | - | - | - | - | - |
|
| 582 |
+
| 3.9297 | 121400 | 0.2791 | - | - | - | - | - | - |
|
| 583 |
+
| 3.9362 | 121600 | 0.2874 | - | - | - | - | - | - |
|
| 584 |
+
| 3.9427 | 121800 | 0.2602 | - | - | - | - | - | - |
|
| 585 |
+
| 3.9491 | 122000 | 0.2988 | - | - | - | - | - | - |
|
| 586 |
+
| 3.9556 | 122200 | 0.2935 | - | - | - | - | - | - |
|
| 587 |
+
| 3.9621 | 122400 | 0.2999 | - | - | - | - | - | - |
|
| 588 |
+
| 3.9686 | 122600 | 0.2930 | - | - | - | - | - | - |
|
| 589 |
+
| 3.9750 | 122800 | 0.2784 | - | - | - | - | - | - |
|
| 590 |
+
| 3.9815 | 123000 | 0.3013 | - | - | - | - | - | - |
|
| 591 |
+
| 3.9880 | 123200 | 0.2919 | - | - | - | - | - | - |
|
| 592 |
+
| 3.9944 | 123400 | 0.3011 | - | - | - | - | - | - |
|
| 593 |
+
|
| 594 |
+
</details>
|
| 595 |
+
|
| 596 |
+
### Framework Versions
|
| 597 |
+
- Python: 3.10.19
|
| 598 |
+
- Sentence Transformers: 5.2.3
|
| 599 |
+
- Transformers: 5.2.0
|
| 600 |
+
- PyTorch: 2.6.0+cu124
|
| 601 |
+
- Accelerate: 1.12.0
|
| 602 |
+
- Datasets: 4.5.0
|
| 603 |
+
- Tokenizers: 0.22.2
|
| 604 |
+
|
| 605 |
+
## Citation
|
| 606 |
+
|
| 607 |
+
### BibTeX
|
| 608 |
+
|
| 609 |
+
#### Sentence Transformers
|
| 610 |
+
```bibtex
|
| 611 |
+
@inproceedings{reimers-2019-sentence-bert,
|
| 612 |
+
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
|
| 613 |
+
author = "Reimers, Nils and Gurevych, Iryna",
|
| 614 |
+
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
|
| 615 |
+
month = "11",
|
| 616 |
+
year = "2019",
|
| 617 |
+
publisher = "Association for Computational Linguistics",
|
| 618 |
+
url = "https://arxiv.org/abs/1908.10084",
|
| 619 |
+
}
|
| 620 |
+
```
|
| 621 |
+
|
| 622 |
+
#### MatryoshkaLoss
|
| 623 |
+
```bibtex
|
| 624 |
+
@misc{kusupati2024matryoshka,
|
| 625 |
+
title={Matryoshka Representation Learning},
|
| 626 |
+
author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
|
| 627 |
+
year={2024},
|
| 628 |
+
eprint={2205.13147},
|
| 629 |
+
archivePrefix={arXiv},
|
| 630 |
+
primaryClass={cs.LG}
|
| 631 |
+
}
|
| 632 |
+
```
|
| 633 |
+
|
| 634 |
+
#### MultipleNegativesRankingLoss
|
| 635 |
+
```bibtex
|
| 636 |
+
@misc{henderson2017efficient,
|
| 637 |
+
title={Efficient Natural Language Response Suggestion for Smart Reply},
|
| 638 |
+
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
|
| 639 |
+
year={2017},
|
| 640 |
+
eprint={1705.00652},
|
| 641 |
+
archivePrefix={arXiv},
|
| 642 |
+
primaryClass={cs.CL}
|
| 643 |
+
}
|
| 644 |
+
```
|
| 645 |
+
|
| 646 |
+
<!--
|
| 647 |
+
## Glossary
|
| 648 |
+
|
| 649 |
+
*Clearly define terms in order to be accessible across audiences.*
|
| 650 |
+
-->
|
| 651 |
+
|
| 652 |
+
<!--
|
| 653 |
+
## Model Card Authors
|
| 654 |
+
|
| 655 |
+
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
|
| 656 |
+
-->
|
| 657 |
+
|
| 658 |
+
<!--
|
| 659 |
+
## Model Card Contact
|
| 660 |
+
|
| 661 |
+
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
|
| 662 |
+
-->
|
epoch4/model/config.json
ADDED
|
@@ -0,0 +1,28 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"add_cross_attention": false,
|
| 3 |
+
"architectures": [
|
| 4 |
+
"BertModel"
|
| 5 |
+
],
|
| 6 |
+
"attention_probs_dropout_prob": 0.1,
|
| 7 |
+
"bos_token_id": null,
|
| 8 |
+
"classifier_dropout": null,
|
| 9 |
+
"dtype": "float32",
|
| 10 |
+
"eos_token_id": null,
|
| 11 |
+
"hidden_act": "gelu",
|
| 12 |
+
"hidden_dropout_prob": 0.1,
|
| 13 |
+
"hidden_size": 768,
|
| 14 |
+
"initializer_range": 0.02,
|
| 15 |
+
"intermediate_size": 3072,
|
| 16 |
+
"is_decoder": false,
|
| 17 |
+
"layer_norm_eps": 1e-12,
|
| 18 |
+
"max_position_embeddings": 512,
|
| 19 |
+
"model_type": "bert",
|
| 20 |
+
"num_attention_heads": 12,
|
| 21 |
+
"num_hidden_layers": 12,
|
| 22 |
+
"pad_token_id": 0,
|
| 23 |
+
"tie_word_embeddings": true,
|
| 24 |
+
"transformers_version": "5.2.0",
|
| 25 |
+
"type_vocab_size": 2,
|
| 26 |
+
"use_cache": true,
|
| 27 |
+
"vocab_size": 64000
|
| 28 |
+
}
|
epoch4/model/config_sentence_transformers.json
ADDED
|
@@ -0,0 +1,14 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"model_type": "SentenceTransformer",
|
| 3 |
+
"__version__": {
|
| 4 |
+
"sentence_transformers": "5.2.3",
|
| 5 |
+
"transformers": "5.2.0",
|
| 6 |
+
"pytorch": "2.6.0+cu124"
|
| 7 |
+
},
|
| 8 |
+
"prompts": {
|
| 9 |
+
"query": "",
|
| 10 |
+
"document": ""
|
| 11 |
+
},
|
| 12 |
+
"default_prompt_name": null,
|
| 13 |
+
"similarity_fn_name": "cosine"
|
| 14 |
+
}
|
epoch4/model/model.safetensors
ADDED
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version https://git-lfs.github.com/spec/v1
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size 540795728
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epoch4/model/modules.json
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[
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{
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"idx": 0,
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"name": "0",
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"path": "",
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"type": "sentence_transformers.models.Transformer"
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epoch4/model/sentence_bert_config.json
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{
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"max_seq_length": 512,
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"do_lower_case": false
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epoch4/model/tokenizer.json
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epoch4/model/tokenizer_config.json
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{
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"never_split": [
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"[بريد]",
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"[مستخدم]",
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"[رابط]"
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epoch4/output.log
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--- RESUMING FROM: /home/skiredj.abderrahman/khalil/sbert_training/output/arabert_20260224_1730/checkpoint-102000 ---
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| 98 |
+
{'loss': '0.2806', 'grad_norm': '3.519', 'learning_rate': '4.987e-07', 'epoch': '3.91'}
|
| 99 |
+
{'loss': '0.2631', 'grad_norm': '3.982', 'learning_rate': '4.627e-07', 'epoch': '3.917'}
|
| 100 |
+
{'loss': '0.2892', 'grad_norm': '3.621', 'learning_rate': '4.267e-07', 'epoch': '3.923'}
|
| 101 |
+
{'loss': '0.2791', 'grad_norm': '4.467', 'learning_rate': '3.908e-07', 'epoch': '3.93'}
|
| 102 |
+
{'loss': '0.2874', 'grad_norm': '4.958', 'learning_rate': '3.548e-07', 'epoch': '3.936'}
|
| 103 |
+
{'loss': '0.2602', 'grad_norm': '3.852', 'learning_rate': '3.188e-07', 'epoch': '3.943'}
|
| 104 |
+
{'loss': '0.2988', 'grad_norm': '2.639', 'learning_rate': '2.829e-07', 'epoch': '3.949'}
|
| 105 |
+
{'loss': '0.2935', 'grad_norm': '1.878', 'learning_rate': '2.469e-07', 'epoch': '3.956'}
|
| 106 |
+
{'loss': '0.2999', 'grad_norm': '3.6', 'learning_rate': '2.109e-07', 'epoch': '3.962'}
|
| 107 |
+
{'loss': '0.293', 'grad_norm': '2.28', 'learning_rate': '1.75e-07', 'epoch': '3.969'}
|
| 108 |
+
{'loss': '0.2784', 'grad_norm': '4.347', 'learning_rate': '1.39e-07', 'epoch': '3.975'}
|
| 109 |
+
{'loss': '0.3013', 'grad_norm': '5.014', 'learning_rate': '1.03e-07', 'epoch': '3.982'}
|
| 110 |
+
{'loss': '0.2919', 'grad_norm': '2.171', 'learning_rate': '6.708e-08', 'epoch': '3.988'}
|
| 111 |
+
{'loss': '0.3011', 'grad_norm': '5.241', 'learning_rate': '3.111e-08', 'epoch': '3.994'}
|
| 112 |
+
{'train_runtime': '4.115e+04', 'train_samples_per_second': '384.4', 'train_steps_per_second': '3.003', 'train_loss': '0.04643', 'epoch': '4'}
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| 113 |
+
model saved successfully
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