diff --git "a/README.md" "b/README.md"
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+++ "b/README.md"
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+---
+base_model: vinai/phobert-base
+datasets: []
+language: []
+library_name: sentence-transformers
+metrics:
+- cosine_accuracy@1
+- cosine_accuracy@3
+- cosine_accuracy@5
+- cosine_accuracy@10
+- cosine_precision@1
+- cosine_precision@3
+- cosine_precision@5
+- cosine_precision@10
+- cosine_recall@1
+- cosine_recall@3
+- cosine_recall@5
+- cosine_recall@10
+- cosine_ndcg@10
+- cosine_mrr@10
+- cosine_map@100
+- dot_accuracy@1
+- dot_accuracy@3
+- dot_accuracy@5
+- dot_accuracy@10
+- dot_precision@1
+- dot_precision@3
+- dot_precision@5
+- dot_precision@10
+- dot_recall@1
+- dot_recall@3
+- dot_recall@5
+- dot_recall@10
+- dot_ndcg@10
+- dot_mrr@10
+- dot_map@100
+pipeline_tag: sentence-similarity
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:7160
+- loss:MultipleNegativesRankingLoss
+widget:
+- source_sentence: bạn tăng đèn vườn 4 ở phòng làm_việc 5 7 lên 5 phần_trăm hộ mình
+ với bôi_đen bạn bỏ dấu ngoặc_kép ra nhé v
+ sentences:
+ - tăng lên quạt_thông_gió thứ 4 lên 73 phần_trăm nhé
+ - kiểm_tra downlight thứ 2 ở ga_ra 3
+ - mình muốn tăng bóng trụ cổng 3
+- source_sentence: nút nhấn 2 phòng nghiên_cứu 1 tăng lên 50 phần_trăm
+ sentences:
+ - bạn giảm cho mình âm_thanh đa vùng thứ 1 được không mình thấy hơi bị sáng quá
+ - bạn tăng đèn vách ở phòng xông_hơi 3 9 lên 4 phần_trăm hộ mình với bôi_đen bạn
+ bỏ dấu ngoặc_kép ra nhé v
+ - tăng thêm đèn treo tường thứ 4 phòng giặt ủi đến 94 phần_trăm
+- source_sentence: trợ_lý béo tăng đèn ốp tường thứ 4 lên 10 phần_trăm ở phòng cuối
+ 5 phòng 3
+ sentences:
+ - tăng hắt trần thứ 2 lên 22 phần_trăm
+ - trợ_lý béo tăng đèn tường thứ 2 lên 7 phần_trăm ở tiền_sảnh 5 10
+ - bật hộ mình hắt tường 1 ở phòng khách 1 nhé
+- source_sentence: bạn có_thể tăng giúp mình độ sáng màn cuốn lên được không
+ sentences:
+ - ơi bạn quạt_treo_tường 3 tăng lên 19 phần_trăm ná
+ - bạn tăng đèn vườn 4 ở phòng làm_việc 5 7 lên 5 phần_trăm hộ mình với bôi_đen bạn
+ bỏ dấu ngoặc_kép ra nhé v
+ - kiểm_tra trạng_thái của tivi ở sân vườn 3 nhé
+- source_sentence: tăng thêm ánh_sáng bóng hắt tường 2 lên mức 51 phần_trăm
+ sentences:
+ - tăng đến đèn 3 phòng hội_nghị thêm 27 phần_trăm
+ - điều_chỉnh hộ âm_thanh đa vùng mức 1
+ - hãy tăng giúp ta cái nóng lạnh 4 ở phòng trẻ_em 5
+model-index:
+- name: SentenceTransformer based on vinai/phobert-base
+ results:
+ - task:
+ type: information-retrieval
+ name: Information Retrieval
+ dataset:
+ name: phobert base train eval
+ type: phobert-base_train_eval
+ metrics:
+ - type: cosine_accuracy@1
+ value: 0.025510204081632654
+ name: Cosine Accuracy@1
+ - type: cosine_accuracy@3
+ value: 0.05102040816326531
+ name: Cosine Accuracy@3
+ - type: cosine_accuracy@5
+ value: 0.08418367346938775
+ name: Cosine Accuracy@5
+ - type: cosine_accuracy@10
+ value: 0.11734693877551021
+ name: Cosine Accuracy@10
+ - type: cosine_precision@1
+ value: 0.025510204081632654
+ name: Cosine Precision@1
+ - type: cosine_precision@3
+ value: 0.019557823129251698
+ name: Cosine Precision@3
+ - type: cosine_precision@5
+ value: 0.020918367346938773
+ name: Cosine Precision@5
+ - type: cosine_precision@10
+ value: 0.020918367346938777
+ name: Cosine Precision@10
+ - type: cosine_recall@1
+ value: 0.0006377551020408163
+ name: Cosine Recall@1
+ - type: cosine_recall@3
+ value: 0.0014526643990929707
+ name: Cosine Recall@3
+ - type: cosine_recall@5
+ value: 0.0025935374149659864
+ name: Cosine Recall@5
+ - type: cosine_recall@10
+ value: 0.005187074829931973
+ name: Cosine Recall@10
+ - type: cosine_ndcg@10
+ value: 0.021206635405851312
+ name: Cosine Ndcg@10
+ - type: cosine_mrr@10
+ value: 0.047738500161969545
+ name: Cosine Mrr@10
+ - type: cosine_map@100
+ value: 0.012687527740444942
+ name: Cosine Map@100
+ - type: dot_accuracy@1
+ value: 0.03826530612244898
+ name: Dot Accuracy@1
+ - type: dot_accuracy@3
+ value: 0.07142857142857142
+ name: Dot Accuracy@3
+ - type: dot_accuracy@5
+ value: 0.10204081632653061
+ name: Dot Accuracy@5
+ - type: dot_accuracy@10
+ value: 0.11479591836734694
+ name: Dot Accuracy@10
+ - type: dot_precision@1
+ value: 0.03826530612244898
+ name: Dot Precision@1
+ - type: dot_precision@3
+ value: 0.02976190476190476
+ name: Dot Precision@3
+ - type: dot_precision@5
+ value: 0.031122448979591835
+ name: Dot Precision@5
+ - type: dot_precision@10
+ value: 0.0288265306122449
+ name: Dot Precision@10
+ - type: dot_recall@1
+ value: 0.0009566326530612247
+ name: Dot Recall@1
+ - type: dot_recall@3
+ value: 0.002225056689342404
+ name: Dot Recall@3
+ - type: dot_recall@5
+ value: 0.003861961451247165
+ name: Dot Recall@5
+ - type: dot_recall@10
+ value: 0.007171201814058956
+ name: Dot Recall@10
+ - type: dot_ndcg@10
+ value: 0.029727966876493785
+ name: Dot Ndcg@10
+ - type: dot_mrr@10
+ value: 0.05998036119209588
+ name: Dot Mrr@10
+ - type: dot_map@100
+ value: 0.014677473888361115
+ name: Dot Map@100
+---
+
+# SentenceTransformer based on vinai/phobert-base
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [vinai/phobert-base](https://huggingface.co/vinai/phobert-base). 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.
+
+## Model Details
+
+### Model Description
+- **Model Type:** Sentence Transformer
+- **Base model:** [vinai/phobert-base](https://huggingface.co/vinai/phobert-base)
+- **Maximum Sequence Length:** 256 tokens
+- **Output Dimensionality:** 768 tokens
+- **Similarity Function:** Cosine Similarity
+
+
+
+
+### Model Sources
+
+- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
+- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
+- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
+
+### Full Model Architecture
+
+```
+SentenceTransformer(
+ (0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: RobertaModel
+ (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})
+)
+```
+
+## Usage
+
+### Direct Usage (Sentence Transformers)
+
+First install the Sentence Transformers library:
+
+```bash
+pip install -U sentence-transformers
+```
+
+Then you can load this model and run inference.
+```python
+from sentence_transformers import SentenceTransformer
+
+# Download from the 🤗 Hub
+model = SentenceTransformer("phongtintruong/clphobert-base")
+# Run inference
+sentences = [
+ 'tăng thêm ánh_sáng bóng hắt tường 2 lên mức 51 phần_trăm',
+ 'tăng đến đèn 3 phòng hội_nghị thêm 27 phần_trăm',
+ 'điều_chỉnh hộ âm_thanh đa vùng mức 1',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 768]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(embeddings, embeddings)
+print(similarities.shape)
+# [3, 3]
+```
+
+
+
+
+
+
+
+## Evaluation
+
+### Metrics
+
+#### Information Retrieval
+* Dataset: `phobert-base_train_eval`
+* Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
+
+| Metric | Value |
+|:--------------------|:-----------|
+| cosine_accuracy@1 | 0.0255 |
+| cosine_accuracy@3 | 0.051 |
+| cosine_accuracy@5 | 0.0842 |
+| cosine_accuracy@10 | 0.1173 |
+| cosine_precision@1 | 0.0255 |
+| cosine_precision@3 | 0.0196 |
+| cosine_precision@5 | 0.0209 |
+| cosine_precision@10 | 0.0209 |
+| cosine_recall@1 | 0.0006 |
+| cosine_recall@3 | 0.0015 |
+| cosine_recall@5 | 0.0026 |
+| cosine_recall@10 | 0.0052 |
+| cosine_ndcg@10 | 0.0212 |
+| cosine_mrr@10 | 0.0477 |
+| **cosine_map@100** | **0.0127** |
+| dot_accuracy@1 | 0.0383 |
+| dot_accuracy@3 | 0.0714 |
+| dot_accuracy@5 | 0.102 |
+| dot_accuracy@10 | 0.1148 |
+| dot_precision@1 | 0.0383 |
+| dot_precision@3 | 0.0298 |
+| dot_precision@5 | 0.0311 |
+| dot_precision@10 | 0.0288 |
+| dot_recall@1 | 0.001 |
+| dot_recall@3 | 0.0022 |
+| dot_recall@5 | 0.0039 |
+| dot_recall@10 | 0.0072 |
+| dot_ndcg@10 | 0.0297 |
+| dot_mrr@10 | 0.06 |
+| dot_map@100 | 0.0147 |
+
+
+
+
+
+## Training Details
+
+### Training Dataset
+
+#### Unnamed Dataset
+
+
+* Size: 7,160 training samples
+* Columns: sentence_0, sentence_1, and sentence_2
+* Approximate statistics based on the first 1000 samples:
+ | | sentence_0 | sentence_1 | sentence_2 |
+ |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
+ | type | string | string | string |
+ | details | - min: 5 tokens
- mean: 13.03 tokens
- max: 32 tokens
| - min: 5 tokens
- mean: 12.62 tokens
- max: 32 tokens
| - min: 4 tokens
- mean: 12.45 tokens
- max: 30 tokens
|
+* Samples:
+ | sentence_0 | sentence_1 | sentence_2 |
+ |:--------------------------------------|:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------|
+ | tăng bóng 3 26 phần_trăm | mình muốn tăng quạt lên 21 phần_trăm | tắt bóng hoa 2 phòng thực_hành 2 |
+ | tăng bóng 3 26 phần_trăm | giúp mình tăng bóng để bàn lên 4 phần_trăm ở phòng ngủ con trai 7 nhé | giảm độ sáng bóng sưởi thứ 4 ấy trợ_lý ảo nó sáng quá |
+ | tăng bóng 3 26 phần_trăm | tăng lên đèn làm_việc cổng chính thêm 10 phần_trăm | cho mình tăng bóng kiểng thứ 2 lên 3 |
+* Loss: [MultipleNegativesRankingLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
+ ```json
+ {
+ "scale": 20.0,
+ "similarity_fct": "cos_sim"
+ }
+ ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 2
+- `per_device_eval_batch_size`: 2
+- `num_train_epochs`: 2
+- `fp16`: True
+- `multi_dataset_batch_sampler`: round_robin
+
+#### All Hyperparameters
+Click to expand
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: steps
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 2
+- `per_device_eval_batch_size`: 2
+- `per_gpu_train_batch_size`: None
+- `per_gpu_eval_batch_size`: None
+- `gradient_accumulation_steps`: 1
+- `eval_accumulation_steps`: None
+- `torch_empty_cache_steps`: None
+- `learning_rate`: 5e-05
+- `weight_decay`: 0.0
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `max_grad_norm`: 1
+- `num_train_epochs`: 2
+- `max_steps`: -1
+- `lr_scheduler_type`: linear
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.0
+- `warmup_steps`: 0
+- `log_level`: passive
+- `log_level_replica`: warning
+- `log_on_each_node`: True
+- `logging_nan_inf_filter`: True
+- `save_safetensors`: True
+- `save_on_each_node`: False
+- `save_only_model`: False
+- `restore_callback_states_from_checkpoint`: False
+- `no_cuda`: False
+- `use_cpu`: False
+- `use_mps_device`: False
+- `seed`: 42
+- `data_seed`: None
+- `jit_mode_eval`: False
+- `use_ipex`: False
+- `bf16`: False
+- `fp16`: True
+- `fp16_opt_level`: O1
+- `half_precision_backend`: auto
+- `bf16_full_eval`: False
+- `fp16_full_eval`: False
+- `tf32`: None
+- `local_rank`: 0
+- `ddp_backend`: None
+- `tpu_num_cores`: None
+- `tpu_metrics_debug`: False
+- `debug`: []
+- `dataloader_drop_last`: False
+- `dataloader_num_workers`: 0
+- `dataloader_prefetch_factor`: None
+- `past_index`: -1
+- `disable_tqdm`: False
+- `remove_unused_columns`: True
+- `label_names`: None
+- `load_best_model_at_end`: False
+- `ignore_data_skip`: False
+- `fsdp`: []
+- `fsdp_min_num_params`: 0
+- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
+- `fsdp_transformer_layer_cls_to_wrap`: None
+- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
+- `deepspeed`: None
+- `label_smoothing_factor`: 0.0
+- `optim`: adamw_torch
+- `optim_args`: None
+- `adafactor`: False
+- `group_by_length`: False
+- `length_column_name`: length
+- `ddp_find_unused_parameters`: None
+- `ddp_bucket_cap_mb`: None
+- `ddp_broadcast_buffers`: False
+- `dataloader_pin_memory`: True
+- `dataloader_persistent_workers`: False
+- `skip_memory_metrics`: True
+- `use_legacy_prediction_loop`: False
+- `push_to_hub`: False
+- `resume_from_checkpoint`: None
+- `hub_model_id`: None
+- `hub_strategy`: every_save
+- `hub_private_repo`: False
+- `hub_always_push`: False
+- `gradient_checkpointing`: False
+- `gradient_checkpointing_kwargs`: None
+- `include_inputs_for_metrics`: False
+- `eval_do_concat_batches`: True
+- `fp16_backend`: auto
+- `push_to_hub_model_id`: None
+- `push_to_hub_organization`: None
+- `mp_parameters`:
+- `auto_find_batch_size`: False
+- `full_determinism`: False
+- `torchdynamo`: None
+- `ray_scope`: last
+- `ddp_timeout`: 1800
+- `torch_compile`: False
+- `torch_compile_backend`: None
+- `torch_compile_mode`: None
+- `dispatch_batches`: None
+- `split_batches`: None
+- `include_tokens_per_second`: False
+- `include_num_input_tokens_seen`: False
+- `neftune_noise_alpha`: None
+- `optim_target_modules`: None
+- `batch_eval_metrics`: False
+- `eval_on_start`: False
+- `eval_use_gather_object`: False
+- `batch_sampler`: batch_sampler
+- `multi_dataset_batch_sampler`: round_robin
+
+Click to expand
+
+| Epoch | Step | Training Loss | phobert-base_train_eval_cosine_map@100 |
+|:------:|:----:|:-------------:|:--------------------------------------:|
+| 0.0003 | 1 | - | 0.0051 |
+| 0.0006 | 2 | - | 0.0051 |
+| 0.0008 | 3 | - | 0.0052 |
+| 0.0011 | 4 | - | 0.0052 |
+| 0.0014 | 5 | - | 0.0053 |
+| 0.0017 | 6 | - | 0.0052 |
+| 0.0020 | 7 | - | 0.0052 |
+| 0.0022 | 8 | - | 0.0051 |
+| 0.0025 | 9 | - | 0.0051 |
+| 0.0028 | 10 | - | 0.0052 |
+| 0.0031 | 11 | - | 0.0051 |
+| 0.0034 | 12 | - | 0.0051 |
+| 0.0036 | 13 | - | 0.0051 |
+| 0.0039 | 14 | - | 0.0050 |
+| 0.0042 | 15 | - | 0.0050 |
+| 0.0045 | 16 | - | 0.0049 |
+| 0.0047 | 17 | - | 0.0050 |
+| 0.0050 | 18 | - | 0.0050 |
+| 0.0053 | 19 | - | 0.0049 |
+| 0.0056 | 20 | - | 0.0049 |
+| 0.0059 | 21 | - | 0.0048 |
+| 0.0061 | 22 | - | 0.0048 |
+| 0.0064 | 23 | - | 0.0048 |
+| 0.0067 | 24 | - | 0.0047 |
+| 0.0070 | 25 | - | 0.0047 |
+| 0.0073 | 26 | - | 0.0048 |
+| 0.0075 | 27 | - | 0.0048 |
+| 0.0078 | 28 | - | 0.0048 |
+| 0.0081 | 29 | - | 0.0049 |
+| 0.0084 | 30 | - | 0.0048 |
+| 0.0087 | 31 | - | 0.0048 |
+| 0.0089 | 32 | - | 0.0048 |
+| 0.0092 | 33 | - | 0.0048 |
+| 0.0095 | 34 | - | 0.0049 |
+| 0.0098 | 35 | - | 0.0049 |
+| 0.0101 | 36 | - | 0.0049 |
+| 0.0103 | 37 | - | 0.0050 |
+| 0.0106 | 38 | - | 0.0049 |
+| 0.0109 | 39 | - | 0.0050 |
+| 0.0112 | 40 | - | 0.0049 |
+| 0.0115 | 41 | - | 0.0049 |
+| 0.0117 | 42 | - | 0.0050 |
+| 0.0120 | 43 | - | 0.0049 |
+| 0.0123 | 44 | - | 0.0050 |
+| 0.0126 | 45 | - | 0.0050 |
+| 0.0128 | 46 | - | 0.0050 |
+| 0.0131 | 47 | - | 0.0050 |
+| 0.0134 | 48 | - | 0.0050 |
+| 0.0137 | 49 | - | 0.0050 |
+| 0.0140 | 50 | - | 0.0051 |
+| 0.0142 | 51 | - | 0.0052 |
+| 0.0145 | 52 | - | 0.0052 |
+| 0.0148 | 53 | - | 0.0052 |
+| 0.0151 | 54 | - | 0.0053 |
+| 0.0154 | 55 | - | 0.0052 |
+| 0.0156 | 56 | - | 0.0052 |
+| 0.0159 | 57 | - | 0.0052 |
+| 0.0162 | 58 | - | 0.0052 |
+| 0.0165 | 59 | - | 0.0053 |
+| 0.0168 | 60 | - | 0.0052 |
+| 0.0170 | 61 | - | 0.0052 |
+| 0.0173 | 62 | - | 0.0053 |
+| 0.0176 | 63 | - | 0.0053 |
+| 0.0179 | 64 | - | 0.0052 |
+| 0.0182 | 65 | - | 0.0053 |
+| 0.0184 | 66 | - | 0.0054 |
+| 0.0187 | 67 | - | 0.0054 |
+| 0.0190 | 68 | - | 0.0054 |
+| 0.0193 | 69 | - | 0.0055 |
+| 0.0196 | 70 | - | 0.0055 |
+| 0.0198 | 71 | - | 0.0054 |
+| 0.0201 | 72 | - | 0.0054 |
+| 0.0204 | 73 | - | 0.0055 |
+| 0.0207 | 74 | - | 0.0055 |
+| 0.0209 | 75 | - | 0.0055 |
+| 0.0212 | 76 | - | 0.0056 |
+| 0.0215 | 77 | - | 0.0056 |
+| 0.0218 | 78 | - | 0.0056 |
+| 0.0221 | 79 | - | 0.0057 |
+| 0.0223 | 80 | - | 0.0058 |
+| 0.0226 | 81 | - | 0.0058 |
+| 0.0229 | 82 | - | 0.0059 |
+| 0.0232 | 83 | - | 0.0059 |
+| 0.0235 | 84 | - | 0.0060 |
+| 0.0237 | 85 | - | 0.0060 |
+| 0.0240 | 86 | - | 0.0060 |
+| 0.0243 | 87 | - | 0.0060 |
+| 0.0246 | 88 | - | 0.0060 |
+| 0.0249 | 89 | - | 0.0059 |
+| 0.0251 | 90 | - | 0.0059 |
+| 0.0254 | 91 | - | 0.0059 |
+| 0.0257 | 92 | - | 0.0058 |
+| 0.0260 | 93 | - | 0.0057 |
+| 0.0263 | 94 | - | 0.0057 |
+| 0.0265 | 95 | - | 0.0057 |
+| 0.0268 | 96 | - | 0.0057 |
+| 0.0271 | 97 | - | 0.0057 |
+| 0.0274 | 98 | - | 0.0056 |
+| 0.0277 | 99 | - | 0.0056 |
+| 0.0279 | 100 | - | 0.0057 |
+| 0.0282 | 101 | - | 0.0056 |
+| 0.0285 | 102 | - | 0.0054 |
+| 0.0288 | 103 | - | 0.0054 |
+| 0.0291 | 104 | - | 0.0054 |
+| 0.0293 | 105 | - | 0.0053 |
+| 0.0296 | 106 | - | 0.0053 |
+| 0.0299 | 107 | - | 0.0054 |
+| 0.0302 | 108 | - | 0.0053 |
+| 0.0304 | 109 | - | 0.0053 |
+| 0.0307 | 110 | - | 0.0054 |
+| 0.0310 | 111 | - | 0.0054 |
+| 0.0313 | 112 | - | 0.0054 |
+| 0.0316 | 113 | - | 0.0055 |
+| 0.0318 | 114 | - | 0.0055 |
+| 0.0321 | 115 | - | 0.0056 |
+| 0.0324 | 116 | - | 0.0058 |
+| 0.0327 | 117 | - | 0.0059 |
+| 0.0330 | 118 | - | 0.0060 |
+| 0.0332 | 119 | - | 0.0061 |
+| 0.0335 | 120 | - | 0.0062 |
+| 0.0338 | 121 | - | 0.0063 |
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+
+