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+---
+base_model: Alibaba-NLP/gte-multilingual-base
+language:
+- hu
+library_name: sentence-transformers
+license: apache-2.0
+metrics:
+- cosine_accuracy
+- dot_accuracy
+- manhattan_accuracy
+- euclidean_accuracy
+- max_accuracy
+pipeline_tag: sentence-similarity
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:1044013
+- loss:MultipleNegativesRankingLoss
+widget:
+- source_sentence: Emberek várnak a lámpánál kerékpárral.
+ sentences:
+ - Az emberek piros lámpánál haladnak.
+ - Az emberek a kerékpárjukon vannak.
+ - Egy fekete kutya úszik a vízben egy teniszlabdával a szájában
+- source_sentence: A kutya a vízben van.
+ sentences:
+ - Két férfi takarítja a havat a tetőről, az egyik egy emelőben ül, a másik pedig
+ a tetőn.
+ - A macska a vízben van, és dühös.
+ - Egy kutya van a vízben, a szájában egy faág.
+- source_sentence: A nő feketét visel.
+ sentences:
+ - Egy barna kutya fröcsköl, ahogy úszik a vízben.
+ - Egy tetoválással rendelkező nő, aki fekete tank tetején néz a földre.
+ - 'Egy kékbe öltözött nő intenzív arckifejezéssel üti a teniszlabdát. A képen:'
+- source_sentence: Az emberek alszanak.
+ sentences:
+ - Három ember beszélget egy városi utcán.
+ - A nő fehéret visel.
+ - Egy apa és a fia ölelgeti alvás közben.
+- source_sentence: Az emberek alszanak.
+ sentences:
+ - Egy feketébe öltözött nő cigarettát és bevásárlótáskát tart a kezében, miközben
+ egy idősebb nő átmegy az utcán.
+ - Egy csoport ember ül egy nyitott, térszerű területen, mögötte nagy bokrok és egy
+ sor viktoriánus stílusú épület, melyek közül sokat a kép jobb oldalán lévő erős
+ elmosódás tesz kivehetetlenné.
+ - Egy apa és a fia ölelgeti alvás közben.
+model-index:
+- name: gte-multilingual-base-hu
+ results:
+ - task:
+ type: triplet
+ name: Triplet
+ dataset:
+ name: all nli dev
+ type: all-nli-dev
+ metrics:
+ - type: cosine_accuracy
+ value: 0.9676
+ name: Cosine Accuracy
+ - type: dot_accuracy
+ value: 0.0324
+ name: Dot Accuracy
+ - type: manhattan_accuracy
+ value: 0.9688
+ name: Manhattan Accuracy
+ - type: euclidean_accuracy
+ value: 0.9676
+ name: Euclidean Accuracy
+ - type: max_accuracy
+ value: 0.9688
+ name: Max Accuracy
+ - task:
+ type: triplet
+ name: Triplet
+ dataset:
+ name: all nli test
+ type: all-nli-test
+ metrics:
+ - type: cosine_accuracy
+ value: 0.9718181818181818
+ name: Cosine Accuracy
+ - type: dot_accuracy
+ value: 0.028181818181818183
+ name: Dot Accuracy
+ - type: manhattan_accuracy
+ value: 0.9725757575757575
+ name: Manhattan Accuracy
+ - type: euclidean_accuracy
+ value: 0.9718181818181818
+ name: Euclidean Accuracy
+ - type: max_accuracy
+ value: 0.9725757575757575
+ name: Max Accuracy
+---
+
+# gte-multilingual-base-hu
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Alibaba-NLP/gte-multilingual-base](https://huggingface.co/Alibaba-NLP/gte-multilingual-base) 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.
+
+## Model Details
+
+### Model Description
+- **Model Type:** Sentence Transformer
+- **Base model:** [Alibaba-NLP/gte-multilingual-base](https://huggingface.co/Alibaba-NLP/gte-multilingual-base)
+- **Maximum Sequence Length:** 8192 tokens
+- **Output Dimensionality:** 768 tokens
+- **Similarity Function:** Cosine Similarity
+- **Training Dataset:**
+ - train
+- **Language:** hu
+- **License:** apache-2.0
+
+### 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': 8192, 'do_lower_case': False}) with Transformer model: NewModel
+ (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+ (2): Normalize()
+)
+```
+
+## 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("karsar/gte-multilingual-base-hu")
+# Run inference
+sentences = [
+ 'Az emberek alszanak.',
+ 'Egy apa és a fia ölelgeti alvás közben.',
+ 'Egy csoport ember ül egy nyitott, térszerű területen, mögötte nagy bokrok és egy sor viktoriánus stílusú épület, melyek közül sokat a kép jobb oldalán lévő erős elmosódás tesz kivehetetlenné.',
+]
+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
+
+#### Triplet
+* Dataset: `all-nli-dev`
+* Evaluated with [TripletEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)
+
+| Metric | Value |
+|:-------------------|:-----------|
+| cosine_accuracy | 0.9676 |
+| dot_accuracy | 0.0324 |
+| manhattan_accuracy | 0.9688 |
+| euclidean_accuracy | 0.9676 |
+| **max_accuracy** | **0.9688** |
+
+#### Triplet
+* Dataset: `all-nli-test`
+* Evaluated with [TripletEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)
+
+| Metric | Value |
+|:-------------------|:-----------|
+| cosine_accuracy | 0.9718 |
+| dot_accuracy | 0.0282 |
+| manhattan_accuracy | 0.9726 |
+| euclidean_accuracy | 0.9718 |
+| **max_accuracy** | **0.9726** |
+
+
+
+
+
+## Training Details
+
+### Training Dataset
+
+#### train
+
+* Dataset: train
+* Size: 1,044,013 training samples
+* Columns: anchor, positive, and negative
+* Approximate statistics based on the first 1000 samples:
+ | | anchor | positive | negative |
+ |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
+ | type | string | string | string |
+ | details | - min: 7 tokens
- mean: 11.73 tokens
- max: 56 tokens
| - min: 6 tokens
- mean: 15.24 tokens
- max: 47 tokens
| - min: 7 tokens
- mean: 16.07 tokens
- max: 53 tokens
|
+* Samples:
+ | anchor | positive | negative |
+ |:---------------------------------------------------------------------------|:----------------------------------------------|:---------------------------------------------------------------|
+ | Egy lóháton ülő ember átugrik egy lerombolt repülőgép felett. | Egy ember a szabadban, lóháton. | Egy ember egy étteremben van, és omlettet rendel. |
+ | Gyerekek mosolyogva és integetett a kamera | Gyermekek vannak jelen | A gyerekek homlokot rántanak |
+ | Egy fiú ugrál a gördeszkát a közepén egy piros híd. | A fiú gördeszkás trükköt csinál. | A fiú korcsolyázik a járdán. |
+* Loss: [MultipleNegativesRankingLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
+ ```json
+ {
+ "scale": 20.0,
+ "similarity_fct": "cos_sim"
+ }
+ ```
+
+### Evaluation Dataset
+
+#### train
+
+* Dataset: train
+* Size: 5,000 evaluation samples
+* Columns: anchor, positive, and negative
+* Approximate statistics based on the first 1000 samples:
+ | | anchor | positive | negative |
+ |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
+ | type | string | string | string |
+ | details | - min: 7 tokens
- mean: 11.73 tokens
- max: 56 tokens
| - min: 6 tokens
- mean: 15.24 tokens
- max: 47 tokens
| - min: 7 tokens
- mean: 16.07 tokens
- max: 53 tokens
|
+* Samples:
+ | anchor | positive | negative |
+ |:---------------------------------------------------------------------------|:----------------------------------------------|:---------------------------------------------------------------|
+ | Egy lóháton ülő ember átugrik egy lerombolt repülőgép felett. | Egy ember a szabadban, lóháton. | Egy ember egy étteremben van, és omlettet rendel. |
+ | Gyerekek mosolyogva és integetett a kamera | Gyermekek vannak jelen | A gyerekek homlokot rántanak |
+ | Egy fiú ugrál a gördeszkát a közepén egy piros híd. | A fiú gördeszkás trükköt csinál. | A fiú korcsolyázik a járdán. |
+* 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
+- `num_train_epochs`: 1
+- `warmup_ratio`: 0.1
+- `bf16`: True
+- `batch_sampler`: no_duplicates
+
+#### All Hyperparameters
+Click to expand
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: steps
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 8
+- `per_device_eval_batch_size`: 8
+- `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`: 1.0
+- `num_train_epochs`: 1
+- `max_steps`: -1
+- `lr_scheduler_type`: linear
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.1
+- `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`: True
+- `fp16`: False
+- `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`: no_duplicates
+- `multi_dataset_batch_sampler`: proportional
+
+Click to expand
+
+| Epoch | Step | Training Loss | train loss | all-nli-dev_max_accuracy | all-nli-test_max_accuracy |
+|:------:|:------:|:-------------:|:----------:|:------------------------:|:-------------------------:|
+| 0 | 0 | - | - | 0.7578 | - |
+| 0.0008 | 100 | 0.8531 | - | - | - |
+| 0.0015 | 200 | 0.938 | - | - | - |
+| 0.0023 | 300 | 0.8788 | - | - | - |
+| 0.0031 | 400 | 0.9619 | - | - | - |
+| 0.0038 | 500 | 0.9634 | - | - | - |
+| 0.0046 | 600 | 1.0995 | - | - | - |
+| 0.0054 | 700 | 0.8266 | - | - | - |
+| 0.0061 | 800 | 0.8647 | - | - | - |
+| 0.0069 | 900 | 0.8123 | - | - | - |
+| 0.0077 | 1000 | 0.7149 | - | - | - |
+| 0.0084 | 1100 | 0.8852 | - | - | - |
+| 0.0092 | 1200 | 0.9001 | - | - | - |
+| 0.0100 | 1300 | 0.8113 | - | - | - |
+| 0.0107 | 1400 | 0.756 | - | - | - |
+| 0.0115 | 1500 | 0.6729 | - | - | - |
+| 0.0123 | 1600 | 0.5763 | - | - | - |
+| 0.0130 | 1700 | 0.6413 | - | - | - |
+| 0.0138 | 1800 | 1.0721 | - | - | - |
+| 0.0146 | 1900 | 0.9248 | - | - | - |
+| 0.0153 | 2000 | 0.9313 | 0.1873 | 0.9518 | - |
+| 0.0161 | 2100 | 0.8165 | - | - | - |
+| 0.0169 | 2200 | 0.7051 | - | - | - |
+| 0.0176 | 2300 | 0.8373 | - | - | - |
+| 0.0184 | 2400 | 0.8337 | - | - | - |
+| 0.0192 | 2500 | 0.6224 | - | - | - |
+| 0.0199 | 2600 | 0.4977 | - | - | - |
+| 0.0207 | 2700 | 0.6843 | - | - | - |
+| 0.0215 | 2800 | 0.4773 | - | - | - |
+| 0.0222 | 2900 | 0.5113 | - | - | - |
+| 0.0230 | 3000 | 0.2415 | - | - | - |
+| 0.0238 | 3100 | 0.2441 | - | - | - |
+| 0.0245 | 3200 | 0.3309 | - | - | - |
+| 0.0253 | 3300 | 0.4765 | - | - | - |
+| 0.0261 | 3400 | 0.4781 | - | - | - |
+| 0.0268 | 3500 | 0.2184 | - | - | - |
+| 0.0276 | 3600 | 0.3596 | - | - | - |
+| 0.0284 | 3700 | 0.655 | - | - | - |
+| 0.0291 | 3800 | 0.6108 | - | - | - |
+| 0.0299 | 3900 | 0.4897 | - | - | - |
+| 0.0307 | 4000 | 0.3217 | 0.3629 | 0.9146 | - |
+| 0.0314 | 4100 | 0.2678 | - | - | - |
+| 0.0322 | 4200 | 0.4772 | - | - | - |
+| 0.0329 | 4300 | 0.46 | - | - | - |
+| 0.0337 | 4400 | 0.3363 | - | - | - |
+| 0.0345 | 4500 | 0.2244 | - | - | - |
+| 0.0352 | 4600 | 0.2708 | - | - | - |
+| 0.0360 | 4700 | 0.288 | - | - | - |
+| 0.0368 | 4800 | 0.4095 | - | - | - |
+| 0.0375 | 4900 | 0.3836 | - | - | - |
+| 0.0383 | 5000 | 0.3999 | - | - | - |
+| 0.0391 | 5100 | 0.2303 | - | - | - |
+| 0.0398 | 5200 | 0.232 | - | - | - |
+| 0.0406 | 5300 | 0.2001 | - | - | - |
+| 0.0414 | 5400 | 0.2552 | - | - | - |
+| 0.0421 | 5500 | 0.2658 | - | - | - |
+| 0.0429 | 5600 | 0.3652 | - | - | - |
+| 0.0437 | 5700 | 0.6644 | - | - | - |
+| 0.0444 | 5800 | 0.4616 | - | - | - |
+| 0.0452 | 5900 | 0.459 | - | - | - |
+| 0.0460 | 6000 | 0.4053 | 0.6328 | 0.8806 | - |
+| 0.0467 | 6100 | 0.3715 | - | - | - |
+| 0.0475 | 6200 | 0.5301 | - | - | - |
+| 0.0483 | 6300 | 0.4412 | - | - | - |
+| 0.0490 | 6400 | 0.3733 | - | - | - |
+| 0.0498 | 6500 | 0.4258 | - | - | - |
+| 0.0506 | 6600 | 0.4896 | - | - | - |
+| 0.0513 | 6700 | 0.4275 | - | - | - |
+| 0.0521 | 6800 | 0.4419 | - | - | - |
+| 0.0529 | 6900 | 0.4671 | - | - | - |
+| 0.0536 | 7000 | 0.4209 | - | - | - |
+| 0.0544 | 7100 | 0.406 | - | - | - |
+| 0.0552 | 7200 | 0.3265 | - | - | - |
+| 0.0559 | 7300 | 0.2712 | - | - | - |
+| 0.0567 | 7400 | 0.3408 | - | - | - |
+| 0.0575 | 7500 | 0.4078 | - | - | - |
+| 0.0582 | 7600 | 0.3304 | - | - | - |
+| 0.0590 | 7700 | 0.2874 | - | - | - |
+| 0.0598 | 7800 | 0.357 | - | - | - |
+| 0.0605 | 7900 | 0.3936 | - | - | - |
+| 0.0613 | 8000 | 0.3239 | 0.5266 | 0.8706 | - |
+| 0.0621 | 8100 | 0.3486 | - | - | - |
+| 0.0628 | 8200 | 0.4123 | - | - | - |
+| 0.0636 | 8300 | 0.7267 | - | - | - |
+| 0.0644 | 8400 | 0.6765 | - | - | - |
+| 0.0651 | 8500 | 0.7502 | - | - | - |
+| 0.0659 | 8600 | 0.8435 | - | - | - |
+| 0.0667 | 8700 | 0.4286 | - | - | - |
+| 0.0674 | 8800 | 0.2898 | - | - | - |
+| 0.0682 | 8900 | 0.4943 | - | - | - |
+| 0.0690 | 9000 | 0.3998 | - | - | - |
+| 0.0697 | 9100 | 0.4484 | - | - | - |
+| 0.0705 | 9200 | 0.4421 | - | - | - |
+| 0.0713 | 9300 | 0.3331 | - | - | - |
+| 0.0720 | 9400 | 0.3354 | - | - | - |
+| 0.0728 | 9500 | 0.5536 | - | - | - |
+| 0.0736 | 9600 | 0.4695 | - | - | - |
+| 0.0743 | 9700 | 0.4275 | - | - | - |
+| 0.0751 | 9800 | 0.4075 | - | - | - |
+| 0.0759 | 9900 | 0.5394 | - | - | - |
+| 0.0766 | 10000 | 0.4852 | 0.4733 | 0.9202 | - |
+| 0.0774 | 10100 | 0.3679 | - | - | - |
+| 0.0782 | 10200 | 0.4251 | - | - | - |
+| 0.0789 | 10300 | 0.262 | - | - | - |
+| 0.0797 | 10400 | 0.384 | - | - | - |
+| 0.0805 | 10500 | 0.3438 | - | - | - |
+| 0.0812 | 10600 | 0.3618 | - | - | - |
+| 0.0820 | 10700 | 0.4057 | - | - | - |
+| 0.0828 | 10800 | 0.5303 | - | - | - |
+| 0.0835 | 10900 | 0.5121 | - | - | - |
+| 0.0843 | 11000 | 0.4173 | - | - | - |
+| 0.0851 | 11100 | 0.409 | - | - | - |
+| 0.0858 | 11200 | 0.6285 | - | - | - |
+| 0.0866 | 11300 | 0.5373 | - | - | - |
+| 0.0874 | 11400 | 0.3423 | - | - | - |
+| 0.0881 | 11500 | 0.5681 | - | - | - |
+| 0.0889 | 11600 | 0.4172 | - | - | - |
+| 0.0897 | 11700 | 0.5511 | - | - | - |
+| 0.0904 | 11800 | 0.4482 | - | - | - |
+| 0.0912 | 11900 | 0.5888 | - | - | - |
+| 0.0920 | 12000 | 0.4315 | 0.8177 | 0.8496 | - |
+| 0.0927 | 12100 | 0.5085 | - | - | - |
+| 0.0935 | 12200 | 0.7179 | - | - | - |
+| 0.0943 | 12300 | 0.72 | - | - | - |
+| 0.0950 | 12400 | 0.4522 | - | - | - |
+| 0.0958 | 12500 | 0.6524 | - | - | - |
+| 0.0966 | 12600 | 0.5518 | - | - | - |
+| 0.0973 | 12700 | 0.5112 | - | - | - |
+| 0.0981 | 12800 | 0.4752 | - | - | - |
+| 0.0988 | 12900 | 0.4075 | - | - | - |
+| 0.0996 | 13000 | 0.7106 | - | - | - |
+| 0.1004 | 13100 | 0.7369 | - | - | - |
+| 0.1011 | 13200 | 0.6002 | - | - | - |
+| 0.1019 | 13300 | 0.3983 | - | - | - |
+| 0.1027 | 13400 | 0.4522 | - | - | - |
+| 0.1034 | 13500 | 0.5373 | - | - | - |
+| 0.1042 | 13600 | 0.6317 | - | - | - |
+| 0.1050 | 13700 | 0.4904 | - | - | - |
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+| 0.1065 | 13900 | 0.4386 | - | - | - |
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+| 0.1992 | 26000 | 0.4098 | 0.3070 | 0.961 | - |
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+| 0.3770 | 49200 | 0.1702 | - | - | - |
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+| 0.3816 | 49800 | 0.1737 | - | - | - |
+| 0.3824 | 49900 | 0.122 | - | - | - |
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+| 0.3854 | 50300 | 0.0942 | - | - | - |
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+| 0.9372 | 122300 | 0.6017 | - | - | - |
+| 0.9379 | 122400 | 0.6283 | - | - | - |
+| 0.9387 | 122500 | 0.758 | - | - | - |
+| 0.9394 | 122600 | 0.694 | - | - | - |
+| 0.9402 | 122700 | 0.7112 | - | - | - |
+| 0.9410 | 122800 | 0.7566 | - | - | - |
+| 0.9417 | 122900 | 0.7118 | - | - | - |
+| 0.9425 | 123000 | 0.6594 | - | - | - |
+| 0.9433 | 123100 | 0.678 | - | - | - |
+| 0.9440 | 123200 | 0.6626 | - | - | - |
+| 0.9448 | 123300 | 0.6724 | - | - | - |
+| 0.9456 | 123400 | 0.7042 | - | - | - |
+| 0.9463 | 123500 | 0.6526 | - | - | - |
+| 0.9471 | 123600 | 0.7039 | - | - | - |
+| 0.9479 | 123700 | 0.6459 | - | - | - |
+| 0.9486 | 123800 | 0.5759 | - | - | - |
+| 0.9494 | 123900 | 0.6211 | - | - | - |
+| 0.9502 | 124000 | 0.6905 | 0.1363 | 0.9698 | - |
+| 0.9509 | 124100 | 0.6422 | - | - | - |
+| 0.9517 | 124200 | 0.668 | - | - | - |
+| 0.9525 | 124300 | 0.5819 | - | - | - |
+| 0.9532 | 124400 | 0.661 | - | - | - |
+| 0.9540 | 124500 | 0.6243 | - | - | - |
+| 0.9548 | 124600 | 0.5936 | - | - | - |
+| 0.9555 | 124700 | 0.5736 | - | - | - |
+| 0.9563 | 124800 | 0.5955 | - | - | - |
+| 0.9571 | 124900 | 0.5115 | - | - | - |
+| 0.9578 | 125000 | 0.5495 | - | - | - |
+| 0.9586 | 125100 | 0.5858 | - | - | - |
+| 0.9594 | 125200 | 0.5644 | - | - | - |
+| 0.9601 | 125300 | 0.5262 | - | - | - |
+| 0.9609 | 125400 | 0.5588 | - | - | - |
+| 0.9617 | 125500 | 0.7303 | - | - | - |
+| 0.9624 | 125600 | 0.6894 | - | - | - |
+| 0.9632 | 125700 | 0.5998 | - | - | - |
+| 0.9640 | 125800 | 0.6216 | - | - | - |
+| 0.9647 | 125900 | 0.5922 | - | - | - |
+| 0.9655 | 126000 | 0.6121 | 0.1362 | 0.9694 | - |
+| 0.9663 | 126100 | 0.5996 | - | - | - |
+| 0.9670 | 126200 | 0.5894 | - | - | - |
+| 0.9678 | 126300 | 0.5347 | - | - | - |
+| 0.9686 | 126400 | 0.5803 | - | - | - |
+| 0.9693 | 126500 | 0.6104 | - | - | - |
+| 0.9701 | 126600 | 0.7723 | - | - | - |
+| 0.9709 | 126700 | 0.7577 | - | - | - |
+| 0.9716 | 126800 | 0.7315 | - | - | - |
+| 0.9724 | 126900 | 0.7341 | - | - | - |
+| 0.9732 | 127000 | 0.7652 | - | - | - |
+| 0.9739 | 127100 | 0.6364 | - | - | - |
+| 0.9747 | 127200 | 0.7246 | - | - | - |
+| 0.9755 | 127300 | 1.2764 | - | - | - |
+| 0.9762 | 127400 | 1.0487 | - | - | - |
+| 0.9770 | 127500 | 0.9892 | - | - | - |
+| 0.9778 | 127600 | 0.767 | - | - | - |
+| 0.9785 | 127700 | 0.7633 | - | - | - |
+| 0.9793 | 127800 | 0.8695 | - | - | - |
+| 0.9801 | 127900 | 0.648 | - | - | - |
+| 0.9808 | 128000 | 0.6227 | 0.1418 | 0.97 | - |
+| 0.9816 | 128100 | 0.5542 | - | - | - |
+| 0.9824 | 128200 | 0.5535 | - | - | - |
+| 0.9831 | 128300 | 0.7327 | - | - | - |
+| 0.9839 | 128400 | 0.6541 | - | - | - |
+| 0.9847 | 128500 | 0.6697 | - | - | - |
+| 0.9854 | 128600 | 0.5708 | - | - | - |
+| 0.9862 | 128700 | 0.6833 | - | - | - |
+| 0.9870 | 128800 | 0.6328 | - | - | - |
+| 0.9877 | 128900 | 0.7026 | - | - | - |
+| 0.9885 | 129000 | 0.5497 | - | - | - |
+| 0.9893 | 129100 | 0.5846 | - | - | - |
+| 0.9900 | 129200 | 0.5708 | - | - | - |
+| 0.9908 | 129300 | 0.7514 | - | - | - |
+| 0.9916 | 129400 | 0.5386 | - | - | - |
+| 0.9923 | 129500 | 0.7419 | - | - | - |
+| 0.9931 | 129600 | 0.8613 | - | - | - |
+| 0.9939 | 129700 | 0.8322 | - | - | - |
+| 0.9946 | 129800 | 0.7606 | - | - | - |
+| 0.9954 | 129900 | 0.7086 | - | - | - |
+| 0.9962 | 130000 | 0.6828 | 0.1488 | 0.9688 | - |
+| 0.9969 | 130100 | 0.8267 | - | - | - |
+| 0.9977 | 130200 | 0.8491 | - | - | - |
+| 0.9985 | 130300 | 1.0619 | - | - | - |
+| 0.9992 | 130400 | 0.8276 | - | - | - |
+| 1.0000 | 130500 | 0.8043 | - | - | - |
+| 1.0 | 130502 | - | - | - | 0.9726 |
+
+