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1_SpladePooling/config.json

@@ -0,0 +1,5 @@
+{
+    "pooling_strategy": "max",
+    "activation_function": "relu",
+    "word_embedding_dimension": 50000
+}

README.md

@@ -1,282 +1,1977 @@
 ---
-language:
-  - en
-  - ko
 tags:
 - sentence-transformers
-- sentence-similarity
 - sparse-encoder
 - sparse
 - splade
-- retrieval
-- multimodal
-- multi-modal
-- crossmodal
-- cross-modal
-- feature-extraction
-- aerospace
-- telepix
+- generated_from_trainer
+- dataset_size:6133378
+- loss:SpladeLoss
+- loss:SparseMultipleNegativesRankingLoss
+- loss:FlopsLoss
+base_model: skt/A.X-Encoder-base
+widget:
+- text: 값에 가중치 a를 곱하여 비용함수에 반영한다. 가시성은 레이더 좌표에서 경로점 좌표를 이은 가시선 벡터가 중간에 지형에 의해 차폐되는지를
+    통해 비용함수에반영된다
+- text: 고리즘 및 인공신경망기법이 사용되었다. 인공신경망은 세대의 증가에 따라 지속적으로 향상하였으며, 수직 풍력터빈의 성능은 독립운전에 비하여
+    최적화된 풍력 타워 내에서 두 배 이상
+- text: "연구에서도 동일하게 적용하였다[9].\n받음각 범위는 –9° ~ 19°이며, 받음각 조절장치를 활용하여 실험모델의 받음각을 1° 간격으로\
+    \ 변화하면서 실험을 수행하였다. \n실험 풍"
+- text: 성을 극복하는 방법을 살펴본다. 우선 반작용휠을 일정한 속도로 회전시키며 펄스 불균일 정보를 측정하는 방법을 알아본다. 그리고 측정된
+    불균일 정보를 토대로 T-방식을 보
+- text: 저고도에서 운용되는 소형 무인항공기의 다수 운용이 신속하고 효과적인 정찰 임무를 수행하는 데 필요한 이유
 pipeline_tag: feature-extraction
 library_name: sentence-transformers
-license: apache-2.0
 ---
-<p align="center">
-    <img src="https://cdn-uploads.huggingface.co/production/uploads/61d6f4a4d49065ee28a1ee7e/V8n2En7BlMNHoi1YXVv8Q.png" width="400"/>
-<p>
-  
-# PIXIE-Splade-v1.0
-**PIXIE-Splade-v1.0** is a **bilingual (ko, en)** [SPLADE](https://arxiv.org/abs/2403.06789) retriever, developed by [TelePIX Co., Ltd](https://telepix.net/). 
-**PIXIE** stands for Tele**PIX** **I**ntelligent **E**mbedding, representing TelePIX’s high-performance embedding technology.
-This model is specifically optimized for retrieval tasks in Korean and English, and demonstrates strong performance in aerospace domain. Through extensive fine-tuning and domain-specific evaluation, PIXIE shows robust retrieval quality for real-world use cases such as document understanding, technical QA, and information retrieval in aerospace and related high-precision fields.
-PIXIE-Splade-v1.0 outputs sparse lexical vectors that are directly 
-compatible with inverted indexing (e.g., Lucene/Elasticsearch). 
-Because each non-zero weight corresponds to a Ko-En subword/token, 
-interpretability is built-in: you can inspect which tokens drive retrieval.
-
-## Why SPLADE for Search?
-- **Inverted Index Ready**: Directly index weighted tokens in standard IR stacks (Lucene/Elasticsearch).
-- **Interpretable by Design**: Top-k contributing tokens per query/document explain *why* a hit matched.
-- **Production-Friendly**: Fast candidate generation at web scale; memory/latency tunable via sparsity thresholds.
-- **Hybrid-Retrieval Friendly**: Combine with dense retrievers via score fusion.
-
-## Model Description
+
+# SPLADE Sparse Encoder
+
+This is a [SPLADE Sparse Encoder](https://www.sbert.net/docs/sparse_encoder/usage/usage.html) model finetuned from [skt/A.X-Encoder-base](https://huggingface.co/skt/A.X-Encoder-base) on the json dataset using the [sentence-transformers](https://www.SBERT.net) library. It maps sentences & paragraphs to a 50000-dimensional sparse vector space   and can be used for semantic search and sparse retrieval.
+## Model Details
+
+### Model Description
 - **Model Type:** SPLADE Sparse Encoder
-<!-- - **Base model:** [Unknown](https://huggingface.co/unknown) -->
-- **Maximum Sequence Length:** 5632 tokens
+- **Base model:** [skt/A.X-Encoder-base](https://huggingface.co/skt/A.X-Encoder-base) <!-- at revision b5c71f3601aedf38372fe21383ac7d04991af187 -->
+- **Maximum Sequence Length:** 2048 tokens
 - **Output Dimensionality:** 50000 dimensions
 - **Similarity Function:** Dot Product
-- **Language:** Bilingual — Korean and English
-- **Domain Specialization:** Aerospace Information Retrieval
-- **License:** apache-2.0 
+- **Training Dataset:**
+    - json
+<!-- - **Language:** Unknown -->
+<!-- - **License:** Unknown -->
+
+### Model Sources
+
+- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
+- **Documentation:** [Sparse Encoder Documentation](https://www.sbert.net/docs/sparse_encoder/usage/usage.html)
+- **Repository:** [Sentence Transformers on GitHub](https://github.com/huggingface/sentence-transformers)
+- **Hugging Face:** [Sparse Encoders on Hugging Face](https://huggingface.co/models?library=sentence-transformers&other=sparse-encoder)
 
 ### Full Model Architecture
 
 ```
 SparseEncoder(
-  (0): MLMTransformer({'max_seq_length': 5632, 'do_lower_case': False, 'architecture': 'ModernBertForMaskedLM'})
+  (0): MLMTransformer({'max_seq_length': 2048, 'do_lower_case': False, 'architecture': 'ModernBertForMaskedLM'})
   (1): SpladePooling({'pooling_strategy': 'max', 'activation_function': 'relu', 'word_embedding_dimension': 50000})
 )
 ```
 
-## Quality Benchmarks
-**PIXIE-Splade-v1.0** is a bilingual embedding model specialized for Korean and English retrieval tasks. 
-It delivers consistently strong performance across a diverse set of domain-specific and open-domain benchmarks in both languages, demonstrating its effectiveness in real-world search applications.
-The table below presents the retrieval performance of several sparse embedding models evaluated on a variety of Korean and English benchmarks.
-We report **Normalized Discounted Cumulative Gain (nDCG@10)** scores, which measure how well a ranked list of documents aligns with ground truth relevance. Higher values indicate better retrieval quality.  
-  
-All evaluations were conducted using the open-source **[Korean-MTEB-Retrieval-Evaluators](https://github.com/BM-K/Korean-MTEB-Retrieval-Evaluators)** codebase to ensure consistent dataset handling, indexing, retrieval, and nDCG@10 computation across models.
-
-### Benchmark Overview and Dataset Descriptions
-| Model Name | # params | STELLA (ko-en) | STELLA (en-en) | MTEB (ko) | BEIR (en) |
-|------|:---:|:---:|:---:|:---:|:---:|
-| telepix/PIXIE-Rune-v1.0 (dense baseline) | 0.5B | 0.5972 | 0.7627 | 0.7603 | 0.5872 |
-| **telepix/PIXIE-Splade-v1.0** | **0.1B** | **0.4148** | **0.6741** | **0.7025** | **0.3760** |
-| | | | | | |
-| opensearch-project/opensearch-neural-sparse-encoding-multilingual-v1 | 0.2B | 0.2618 | 0.7055 | 0.5358 | 0.3756 |
-| naver/splade-v3 | 0.1B | N/A | 0.7836 | 0.0685 | 0.3680 |
-| BM25 | N/A | N/A | 0.6589 | 0.5071 | 0.4074 |
-
-To better interpret the evaluation results above, we briefly describe the characteristics and evaluation intent of each benchmark suite used in this comparison.
-Each benchmark is designed to assess different aspects of retrieval capability, ranging from domain-specific technical understanding to open-domain and multilingual generalization.
-
-#### STELLA
-[STELLA](https://arxiv.org/abs/2601.03496) is an aerospace-domain Information Retrieval (IR) benchmark constructed from NASA Technical Reports Server (NTRS) documents. It is designed to evaluate both:
-
-- **Lexical matching** ability (does the retriever benefit from exact technical terms? | TCQ)
-- **Semantic matching** ability (can the retriever match concepts even when technical terms are not explicitly used? | TAQ).
-
-STELLA provides **dual-type synthetic queries** and a **cross-lingual extension** for multilingual evaluation while keeping the corpus in English.
-
-#### 6 Datasets of MTEB (Korean)
-Descriptions of the benchmark datasets used for evaluation are as follows:
-- **Ko-StrategyQA**  
-  A Korean multi-hop open-domain question answering dataset designed for complex reasoning over multiple documents.
-- **AutoRAGRetrieval**  
-  A domain-diverse retrieval dataset covering finance, government, healthcare, legal, and e-commerce sectors.
-- **MIRACLRetrieval**  
-  A document retrieval benchmark built on Korean Wikipedia articles.
-- **PublicHealthQA**  
-  A retrieval dataset focused on medical and public health topics.
-- **BelebeleRetrieval**  
-  A dataset for retrieving relevant content from web and news articles in Korean.
-- **MultiLongDocRetrieval**  
-  A long-document retrieval benchmark based on Korean Wikipedia and mC4 corpus.
-
-#### 7 Datasets of BEIR (English)
-Descriptions of the benchmark datasets used for evaluation are as follows:
-- **ArguAna**  
-  A dataset for argument retrieval based on claim-counterclaim pairs from online debate forums.
-- **FEVER**  
-  A fact verification dataset using Wikipedia for evidence-based claim validation.
-- **FiQA-2018**  
-  A retrieval benchmark tailored to the finance domain with real-world questions and answers.
-- **HotpotQA**  
-  A multi-hop open-domain QA dataset requiring reasoning across multiple documents.
-- **MSMARCO**  
-  A large-scale benchmark using real Bing search queries and corresponding web documents.
-- **NQ**  
-  A Google QA dataset where user questions are answered using Wikipedia articles.
-- **SCIDOCS**  
-  A citation-based document retrieval dataset focused on scientific papers.
-
-## Direct Use (Inverted-Index Retrieval)
+## 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
-import torch
-import numpy as np
-from collections import defaultdict
-from typing import Dict, List, Tuple
-from transformers import AutoTokenizer
 from sentence_transformers import SparseEncoder
 
-model_name= 'telepix/PIXIE-Splade-v1.0'
-device = "cuda" if torch.cuda.is_available() else "cpu"
-
-def _to_dense_numpy(x) -> np.ndarray:
-    if hasattr(x, "to_dense"):
-        return x.to_dense().float().cpu().numpy()
-    if isinstance(x, torch.Tensor):
-        return x.float().cpu().numpy()
-    return np.asarray(x)
-
-def _filter_special_ids(ids: List[int], tokenizer) -> List[int]:
-    special = set(getattr(tokenizer, "all_special_ids", []) or [])
-    return [i for i in ids if i not in special]
-
-def build_inverted_index(
-    model: SparseEncoder,
-    tokenizer,
-    documents: List[str],
-    batch_size: int = 8,
-    min_weight: float = 0.0,
-) -> Tuple[Dict[int, List[Tuple[int, float]]], List[str]]:
-    with torch.no_grad():
-        doc_emb = model.encode_document(documents, batch_size=batch_size)
-    doc_dense = _to_dense_numpy(doc_emb)
-
-    index: Dict[int, List[Tuple[int, float]]] = defaultdict(list)
-
-    for doc_idx, vec in enumerate(doc_dense):
-        nz = np.flatnonzero(vec > min_weight)
-        nz = _filter_special_ids(nz.tolist(), tokenizer)
-
-        for token_id in nz:
-            index[token_id].append((doc_idx, float(vec[token_id])))
-
-    return index
-
-def splade_token_overlap_inverted(
-    model: SparseEncoder,
-    tokenizer,
-    inverted_index: Dict[int, List[Tuple[int, float]]],
-    documents: List[str],
-    queries: List[str],
-    top_k_docs: int = 3,
-    top_k_tokens: int = 5,
-    min_weight: float = 0.0,
-):
-    for qi, qtext in enumerate(queries):
-        with torch.no_grad():
-            q_vec = model.encode_query(qtext)
-        q_vec = _to_dense_numpy(q_vec).ravel()
-
-        q_nz = np.flatnonzero(q_vec > min_weight).tolist()
-        q_nz = _filter_special_ids(q_nz, tokenizer)
-
-        scores: Dict[int, float] = defaultdict(float)
-        per_doc_contrib: Dict[int, Dict[int, Tuple[float, float, float]]] = defaultdict(dict)
-
-        for tid in q_nz:
-            qw = float(q_vec[tid])
-            postings = inverted_index.get(tid, [])
-            for doc_idx, dw in postings:
-                prod = qw * dw
-                scores[doc_idx] += prod
-                per_doc_contrib[doc_idx][tid] = (qw, dw, prod)
-
-        ranked = sorted(scores.items(), key=lambda x: x[1], reverse=True)[:top_k_docs]
-
-        print("\n" + "="*60)
-        print(f"[Query {qi + 1}] {qtext}")
-        print("="*60)
-
-        if not ranked:
-            print("→ No matching documents found.")
-            continue
-
-        for rank, (doc_idx, score) in enumerate(ranked, start=1):
-            doc = documents[doc_idx]
-            print(f"\n→ Rank {rank} | Score: {score:.4f}")
-            print(f"  Document: \"{doc}\"")
-
-            contrib = per_doc_contrib[doc_idx]
-            if not contrib:
-                print("  (No overlapping tokens)")
-                continue
-
-            top = sorted(contrib.items(), key=lambda kv: kv[1][2], reverse=True)[:top_k_tokens]
-            token_ids = [tid for tid, _ in top]
-            tokens = tokenizer.convert_ids_to_tokens(token_ids)
-
-            print(f"  [Top {top_k_tokens} Contributing Tokens]")
-            print(f"  {'Token':<20} {'Score (qw*dw)':>15}")
-            print(f"  {'-'*35}")
-            for (tid, (qw, dw, prod)), tok in zip(top, tokens):
-                clean_tok = tok.replace("##", "")
-                print(f"  {clean_tok:<20} {prod:15.4f}")
-
-if __name__ == "__main__":
-    print(f"Loading model: {model_name}...")
-    model = SparseEncoder(model_name).to(device)
-    tokenizer = AutoTokenizer.from_pretrained(model_name)
-
-    documents = [
-        "텔레픽스는 위성 데이터를 분석하여 해양, 농업 등 다양한 분야에 솔루션을 제공합니다.",
-        "고해상도 광학 위성 영상은 국방 및 정찰 목적으로 중요하게 활용됩니다.",
-        "TelePIX provides advanced solutions by analyzing satellite data for ocean and agriculture.",
-        "High-resolution optical satellite imagery is critical for defense and reconnaissance.",
-        "Space economy creates new value through the utilization of space-based data."
-    ]
-
-    # Cross-lingual test queries :)
-    queries = [
-        "텔레픽스는 어떤 산업 분야에서 위성 데이터를 활용하나요?",
-        "Utilization of satellite imagery for defense",
-    ]
-
-    print("Building inverted index...")
-    inverted_index = build_inverted_index(
-        model=model,
-        tokenizer=tokenizer,
-        documents=documents,
-        batch_size=4,
-        min_weight=0.01, # 노이즈 제거를 위해 약간의 threshold를 줄 수 있습니다.
-    )
-
-    splade_token_overlap_inverted(
-        model=model,
-        tokenizer=tokenizer,
-        inverted_index=inverted_index,
-        documents=documents,
-        queries=queries,
-        top_k_docs=2,
-        top_k_tokens=5
-    )
+# Download from the 🤗 Hub
+model = SparseEncoder("sparse_encoder_model_id")
+# Run inference
+sentences = [
+    '저고도에서 운용되는 소형 무인항공기의 다수 운용이 신속하고 효과적인 정찰 임무를 수행하는 데 필요한 이유',
+    '형 무인항공기도 다양하게 활용되고 있다. 저고도에서 운용되는 소형 무인항공기는 개별적 운용보다는 다수의 무인항공기를 동시에 사용하여야 신속하고 효과적인 정찰 임무를 수 행할 수가 ���다',
+    '색 및 정찰 임무를 수행하는데 있어서 무인항공기의 운용 대수, 비행고도 등 운용 조건에 따라 임무 수행의 효율성과 효과성은 크게 변경될 수 있다. 하지만 어떤 운용조건이 가장 합리',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 50000]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(embeddings, embeddings)
+print(similarities)
+# tensor([[ 34.3231,  46.3908,  19.9883],
+#         [ 46.3908, 162.7550,  54.5493],
+#         [ 19.9883,  54.5493, 129.3976]])
 ```
 
-## License
-The PIXIE-Splade-v1.0 model is licensed under Apache License 2.0.
+<!--
+### Direct Usage (Transformers)
+
+<details><summary>Click to see the direct usage in Transformers</summary>
+
+</details>
+-->
+
+<!--
+### Downstream Usage (Sentence Transformers)
+
+You can finetune this model on your own dataset.
+
+<details><summary>Click to expand</summary>
+
+</details>
+-->
+
+<!--
+### Out-of-Scope Use
+
+*List how the model may foreseeably be misused and address what users ought not to do with the model.*
+-->
+
+<!--
+## Bias, Risks and Limitations
+
+*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
+-->
+
+<!--
+### Recommendations
+
+*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
+-->
+
+## Training Details
+
+### Training Dataset
+
+#### json
+
+* Dataset: json
+* Size: 6,133,378 training samples
+* Columns: <code>anchor</code>, <code>positive</code>, <code>negative_1</code>, <code>negative_2</code>, <code>negative_3</code>, <code>negative_4</code>, and <code>negative_5</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | anchor                                                                             | positive                                                                           | negative_1                                                                         | negative_2                                                                        | negative_3                                                                         | negative_4                                                                         | negative_5                                                                         |
+  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
+  | type    | string                                                                             | string                                                                             | string                                                                             | string                                                                            | string                                                                             | string                                                                             | string                                                                             |
+  | details | <ul><li>min: 10 tokens</li><li>mean: 30.15 tokens</li><li>max: 63 tokens</li></ul> | <ul><li>min: 29 tokens</li><li>mean: 51.86 tokens</li><li>max: 73 tokens</li></ul> | <ul><li>min: 26 tokens</li><li>mean: 51.67 tokens</li><li>max: 69 tokens</li></ul> | <ul><li>min: 29 tokens</li><li>mean: 51.8 tokens</li><li>max: 71 tokens</li></ul> | <ul><li>min: 27 tokens</li><li>mean: 51.55 tokens</li><li>max: 71 tokens</li></ul> | <ul><li>min: 27 tokens</li><li>mean: 51.61 tokens</li><li>max: 70 tokens</li></ul> | <ul><li>min: 27 tokens</li><li>mean: 51.76 tokens</li><li>max: 69 tokens</li></ul> |
+* Samples:
+  | anchor                                                                     | positive                                                                                              | negative_1                                                                                              | negative_2                                                                                            | negative_3                                                                                               | negative_4                                                                                              | negative_5                                                                                           |
+  |:---------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------|
+  | <code>본 논문을 검토해주신 심사위원님들과 편집에 도움을 주신 거목문화사에 감사드립니다</code>                  | <code>능성이 있다고 판단된다. <br>감사의 글 <br>본 논문을 검토해주신 심사위원님들과 편집에 도움을 주신 거목문화사에 감사드립니다.</code>                | <code>없이 정상 운용 중에 있다. <br>감사의 글 <br>이 논문에 대하여 중요한 지적과 코멘트를 해주시고, 세심한 심사를 해주신 익명의 심사위원님들께 감사드립니다.</code> | <code>의 지원을 받았으며, 이에 감사드립니다. 논문의표현을 명확히 하는데 도움을 주신 익명의 심사자분들께 감사드립니다.</code>                          | <code>화에 활용되기를 기대한다. <br>감사의 글 <br>본 연구는 한국연구재단의 지원을 받아 수행되었습니다(NRF-2022R1A2C1092602).</code>            | <code>자 및 심사위원분들께 감사드립니다. 본 논문은 기상청 “수치예보·지진 업무 지원 및 활용연구” 과제의 지원을 받아 수행되었습니다.</code>                   | <code>자분들과 발간을위해 노력해주신 논문 심사위원분들 및 대한원격탐사학회 편집이사, 편집간사님께 깊은 감사의 말씀을 드립니다.</code>                     |
+  | <code>양한 노력이 필요할 것으로 사료된다 사사 본 논문은 농촌진흥청 공동연구사업의 지원을 받았으며 이에 감사드립니다</code> | <code>양한 노력이 필요할 것으로 사료된다.<br>사사<br>본 논문은 농촌진흥청 공동연구사업(과제번호: PJ01415301)의 지원을 받았으며, 이에 감사드립니다.</code> | <code>관측이 이루어 질 것으로 기대된다.<br>사사<br>본 논문은 농촌진흥청 공동연구사업(과제번호: PJ01382101)의 지원을 받았으며, 이에 감사드립니다.</code>    | <code>활용성을 향상시켜야 할 것이다.<br>사사<br>본 논문은 농촌진흥청 공동연구사업(과제번호: PJ016768)의 지원을 받아 수행되었으며, 이에 감사드립니다.</code> | <code>기초자료로 사용될 것으로 판단된다.<br>사사<br>본 논문은 농촌진흥청 공동연구사업(과제번호: PJ013821012021)의 지원을 받았으며, 이에 감사드립니다.</code> | <code>해를 바탕으로 분석되어야 하겠다.<br>사사<br>이 논문은 농촌진흥청 공동연구사업(과제번호: PJ015103052022)의 지원을 받았으며, 이에 감사드립니다.</code> | <code>의 개발이 요구될 것으로 판단된다.<br>사사<br>본 논문은 농촌진흥청 공동연구사업(과제번호: PJ01478701)의 지원을 받았으며, 이에 감사드립니다.</code> |
+  | <code>추가로 실험되어야 할 것으로 생각된다 농촌진흥청 공동연구사업 PJ01427401 지원 감사드립니다</code>        | <code>추가로 실험되어야 할 것으로 생각된다.<br>사사<br>본 논문은 농촌진흥청 공동연구사업(과제번호:PJ01427401)의 지원을 받았으며, 이에 감사 드립니다</code> | <code>의 영향 등을 분석할 예정이다. 사 사<br>본 논문은 농촌진흥청 공동연구사업(과제번호: PJ01350004)의 지원을 받았으며, 이에 감사드립니다.</code>        | <code>관측이 이루어 질 것으로 기대된다.<br>사사<br>본 논문은 농촌진흥청 공동연구사업(과제번호: PJ01382101)의 지원을 받았으며, 이에 감사드립니다.</code>  | <code>는지도 추가 연구로 진행될 예정이다.<br>사사<br>본 논문은 농촌진흥청 연구사업(과제번호: PJ0162342021)의 지원에 의해 이루어진 것임.</code>         | <code>I2018-05510)의 지원을 받아 수행된 연구임. 또한,이 논문은 농촌진흥청 공동연구사업(PJ014787042020)의 지원을 받았으며, 이에 감사드립니다.</code>  | <code>연구는 계속되어야 할 것으로 사료된다.<br>사 사<br>본 논문은 농촌진흥청 공동연구사업(과제번호: PJ01382101)의 지원을 받아 수행되었습니다</code>    |
+* Loss: [<code>SpladeLoss</code>](https://sbert.net/docs/package_reference/sparse_encoder/losses.html#spladeloss) with these parameters:
+  ```json
+  {
+      "loss": "SparseMultipleNegativesRankingLoss(scale=1.0, similarity_fct='dot_score', gather_across_devices=False)",
+      "document_regularizer_weight": 3e-05,
+      "query_regularizer_weight": 5e-05
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `per_device_train_batch_size`: 6
+- `gradient_accumulation_steps`: 4
+- `learning_rate`: 2e-05
+- `warmup_ratio`: 0.05
+- `bf16`: True
+- `ddp_find_unused_parameters`: True
+- `ddp_timeout`: 7200
+- `batch_sampler`: no_duplicates
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: no
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 6
+- `per_device_eval_batch_size`: 8
+- `per_gpu_train_batch_size`: None
+- `per_gpu_eval_batch_size`: None
+- `gradient_accumulation_steps`: 4
+- `eval_accumulation_steps`: None
+- `torch_empty_cache_steps`: None
+- `learning_rate`: 2e-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`: 3
+- `max_steps`: -1
+- `lr_scheduler_type`: linear
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.05
+- `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`: True
+- `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}
+- `tp_size`: 0
+- `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`: True
+- `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`: None
+- `hub_always_push`: False
+- `gradient_checkpointing`: False
+- `gradient_checkpointing_kwargs`: None
+- `include_inputs_for_metrics`: False
+- `include_for_metrics`: []
+- `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`: 7200
+- `torch_compile`: False
+- `torch_compile_backend`: None
+- `torch_compile_mode`: 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
+- `use_liger_kernel`: False
+- `eval_use_gather_object`: False
+- `average_tokens_across_devices`: False
+- `prompts`: None
+- `batch_sampler`: no_duplicates
+- `multi_dataset_batch_sampler`: proportional
+- `router_mapping`: {}
+- `learning_rate_mapping`: {}
+
+</details>
+
+### Training Logs
+<details><summary>Click to expand</summary>
+
+| Epoch  | Step   | Training Loss |
+|:------:|:------:|:-------------:|
+| 0.0016 | 100    | 16244.8113    |
+| 0.0031 | 200    | 14996.3725    |
+| 0.0047 | 300    | 9421.6025     |
+| 0.0063 | 400    | 3552.6466     |
+| 0.0078 | 500    | 814.9219      |
+| 0.0094 | 600    | 221.6705      |
+| 0.0110 | 700    | 92.4095       |
+| 0.0125 | 800    | 64.8605       |
+| 0.0141 | 900    | 27.5528       |
+| 0.0157 | 1000   | 15.4449       |
+| 0.0172 | 1100   | 12.8785       |
+| 0.0188 | 1200   | 9.3655        |
+| 0.0203 | 1300   | 5.7947        |
+| 0.0219 | 1400   | 4.4217        |
+| 0.0235 | 1500   | 2.2635        |
+| 0.0250 | 1600   | 1.6383        |
+| 0.0266 | 1700   | 1.818         |
+| 0.0282 | 1800   | 2.5322        |
+| 0.0297 | 1900   | 1.8665        |
+| 0.0313 | 2000   | 1.7604        |
+| 0.0329 | 2100   | 1.8703        |
+| 0.0344 | 2200   | 2.2561        |
+| 0.0360 | 2300   | 1.1901        |
+| 0.0376 | 2400   | 1.3095        |
+| 0.0391 | 2500   | 1.1753        |
+| 0.0407 | 2600   | 1.2317        |
+| 0.0423 | 2700   | 1.0613        |
+| 0.0438 | 2800   | 1.727         |
+| 0.0454 | 2900   | 1.1044        |
+| 0.0470 | 3000   | 0.99          |
+| 0.0485 | 3100   | 1.0261        |
+| 0.0501 | 3200   | 1.0384        |
+| 0.0517 | 3300   | 1.051         |
+| 0.0532 | 3400   | 1.0883        |
+| 0.0548 | 3500   | 1.1632        |
+| 0.0563 | 3600   | 1.2008        |
+| 0.0579 | 3700   | 1.18          |
+| 0.0595 | 3800   | 1.1115        |
+| 0.0610 | 3900   | 1.2229        |
+| 0.0626 | 4000   | 1.0997        |
+| 0.0642 | 4100   | 1.2086        |
+| 0.0657 | 4200   | 1.0919        |
+| 0.0673 | 4300   | 1.0244        |
+| 0.0689 | 4400   | 1.1116        |
+| 0.0704 | 4500   | 1.0373        |
+| 0.0720 | 4600   | 1.0658        |
+| 0.0736 | 4700   | 1.0987        |
+| 0.0751 | 4800   | 1.0947        |
+| 0.0767 | 4900   | 1.0875        |
+| 0.0783 | 5000   | 1.1346        |
+| 0.0798 | 5100   | 1.1292        |
+| 0.0814 | 5200   | 0.9917        |
+| 0.0830 | 5300   | 1.0155        |
+| 0.0845 | 5400   | 0.9953        |
+| 0.0861 | 5500   | 1.1092        |
+| 0.0877 | 5600   | 0.9991        |
+| 0.0892 | 5700   | 1.0246        |
+| 0.0908 | 5800   | 1.0436        |
+| 0.0923 | 5900   | 0.9698        |
+| 0.0939 | 6000   | 1.0185        |
+| 0.0955 | 6100   | 1.0084        |
+| 0.0970 | 6200   | 0.9925        |
+| 0.0986 | 6300   | 0.9053        |
+| 0.1002 | 6400   | 0.8762        |
+| 0.1017 | 6500   | 0.8794        |
+| 0.1033 | 6600   | 0.9318        |
+| 0.1049 | 6700   | 0.9518        |
+| 0.1064 | 6800   | 0.9246        |
+| 0.1080 | 6900   | 0.9226        |
+| 0.1096 | 7000   | 1.0066        |
+| 0.1111 | 7100   | 0.8303        |
+| 0.1127 | 7200   | 0.9265        |
+| 0.1143 | 7300   | 0.9143        |
+| 0.1158 | 7400   | 0.8936        |
+| 0.1174 | 7500   | 0.9081        |
+| 0.1190 | 7600   | 0.8753        |
+| 0.1205 | 7700   | 0.8978        |
+| 0.1221 | 7800   | 0.8788        |
+| 0.1237 | 7900   | 0.8241        |
+| 0.1252 | 8000   | 0.8638        |
+| 0.1268 | 8100   | 0.826         |
+| 0.1283 | 8200   | 0.8427        |
+| 0.1299 | 8300   | 0.8508        |
+| 0.1315 | 8400   | 0.8363        |
+| 0.1330 | 8500   | 0.8271        |
+| 0.1346 | 8600   | 0.8813        |
+| 0.1362 | 8700   | 0.8844        |
+| 0.1377 | 8800   | 0.8977        |
+| 0.1393 | 8900   | 0.8685        |
+| 0.1409 | 9000   | 0.8001        |
+| 0.1424 | 9100   | 0.8375        |
+| 0.1440 | 9200   | 0.7616        |
+| 0.1456 | 9300   | 0.8178        |
+| 0.1471 | 9400   | 0.7852        |
+| 0.1487 | 9500   | 0.8447        |
+| 0.1503 | 9600   | 0.8703        |
+| 0.1518 | 9700   | 0.7935        |
+| 0.1534 | 9800   | 0.8368        |
+| 0.1550 | 9900   | 0.8424        |
+| 0.1565 | 10000  | 0.7916        |
+| 0.1581 | 10100  | 0.7628        |
+| 0.1597 | 10200  | 0.9058        |
+| 0.1612 | 10300  | 0.8397        |
+| 0.1628 | 10400  | 0.8112        |
+| 0.1643 | 10500  | 0.784         |
+| 0.1659 | 10600  | 0.7526        |
+| 0.1675 | 10700  | 0.7964        |
+| 0.1690 | 10800  | 0.7964        |
+| 0.1706 | 10900  | 0.7561        |
+| 0.1722 | 11000  | 0.81          |
+| 0.1737 | 11100  | 0.7754        |
+| 0.1753 | 11200  | 0.7899        |
+| 0.1769 | 11300  | 0.7358        |
+| 0.1784 | 11400  | 0.7459        |
+| 0.1800 | 11500  | 0.7711        |
+| 0.1816 | 11600  | 0.7457        |
+| 0.1831 | 11700  | 0.6877        |
+| 0.1847 | 11800  | 0.751         |
+| 0.1863 | 11900  | 0.6906        |
+| 0.1878 | 12000  | 0.7207        |
+| 0.1894 | 12100  | 0.767         |
+| 0.1910 | 12200  | 0.7843        |
+| 0.1925 | 12300  | 0.7579        |
+| 0.1941 | 12400  | 0.7407        |
+| 0.1957 | 12500  | 0.7675        |
+| 0.1972 | 12600  | 0.7664        |
+| 0.1988 | 12700  | 0.7303        |
+| 0.2003 | 12800  | 0.7588        |
+| 0.2019 | 12900  | 0.7472        |
+| 0.2035 | 13000  | 0.7537        |
+| 0.2050 | 13100  | 0.7457        |
+| 0.2066 | 13200  | 0.7147        |
+| 0.2082 | 13300  | 0.7303        |
+| 0.2097 | 13400  | 0.7112        |
+| 0.2113 | 13500  | 0.7268        |
+| 0.2129 | 13600  | 0.7063        |
+| 0.2144 | 13700  | 0.7578        |
+| 0.2160 | 13800  | 0.6814        |
+| 0.2176 | 13900  | 0.7841        |
+| 0.2191 | 14000  | 0.7294        |
+| 0.2207 | 14100  | 0.6652        |
+| 0.2223 | 14200  | 0.698         |
+| 0.2238 | 14300  | 0.6825        |
+| 0.2254 | 14400  | 0.7365        |
+| 0.2270 | 14500  | 0.7525        |
+| 0.2285 | 14600  | 0.739         |
+| 0.2301 | 14700  | 0.7418        |
+| 0.2317 | 14800  | 0.717         |
+| 0.2332 | 14900  | 0.6951        |
+| 0.2348 | 15000  | 0.6137        |
+| 0.2363 | 15100  | 0.6708        |
+| 0.2379 | 15200  | 0.7128        |
+| 0.2395 | 15300  | 0.6664        |
+| 0.2410 | 15400  | 0.706         |
+| 0.2426 | 15500  | 0.7061        |
+| 0.2442 | 15600  | 0.7778        |
+| 0.2457 | 15700  | 0.7449        |
+| 0.2473 | 15800  | 0.7875        |
+| 0.2489 | 15900  | 0.7922        |
+| 0.2504 | 16000  | 0.734         |
+| 0.2520 | 16100  | 0.7408        |
+| 0.2536 | 16200  | 0.7792        |
+| 0.2551 | 16300  | 0.7408        |
+| 0.2567 | 16400  | 0.726         |
+| 0.2583 | 16500  | 0.7087        |
+| 0.2598 | 16600  | 0.7567        |
+| 0.2614 | 16700  | 0.6703        |
+| 0.2630 | 16800  | 0.7594        |
+| 0.2645 | 16900  | 0.7764        |
+| 0.2661 | 17000  | 0.7142        |
+| 0.2677 | 17100  | 0.6808        |
+| 0.2692 | 17200  | 0.6889        |
+| 0.2708 | 17300  | 0.7414        |
+| 0.2723 | 17400  | 0.7563        |
+| 0.2739 | 17500  | 0.7818        |
+| 0.2755 | 17600  | 0.7538        |
+| 0.2770 | 17700  | 0.7004        |
+| 0.2786 | 17800  | 0.8239        |
+| 0.2802 | 17900  | 0.7227        |
+| 0.2817 | 18000  | 0.7485        |
+| 0.2833 | 18100  | 0.753         |
+| 0.2849 | 18200  | 0.7693        |
+| 0.2864 | 18300  | 0.7226        |
+| 0.2880 | 18400  | 0.7692        |
+| 0.2896 | 18500  | 0.7658        |
+| 0.2911 | 18600  | 0.7407        |
+| 0.2927 | 18700  | 0.8059        |
+| 0.2943 | 18800  | 0.8043        |
+| 0.2958 | 18900  | 0.8128        |
+| 0.2974 | 19000  | 0.7007        |
+| 0.2990 | 19100  | 0.7464        |
+| 0.3005 | 19200  | 0.8056        |
+| 0.3021 | 19300  | 0.7446        |
+| 0.3037 | 19400  | 0.7894        |
+| 0.3052 | 19500  | 0.643         |
+| 0.3068 | 19600  | 0.7132        |
+| 0.3083 | 19700  | 0.7687        |
+| 0.3099 | 19800  | 0.6915        |
+| 0.3115 | 19900  | 0.7061        |
+| 0.3130 | 20000  | 0.7368        |
+| 0.3146 | 20100  | 0.6851        |
+| 0.3162 | 20200  | 0.7286        |
+| 0.3177 | 20300  | 0.6868        |
+| 0.3193 | 20400  | 0.6745        |
+| 0.3209 | 20500  | 0.8097        |
+| 0.3224 | 20600  | 0.6915        |
+| 0.3240 | 20700  | 0.7654        |
+| 0.3256 | 20800  | 0.7396        |
+| 0.3271 | 20900  | 0.7502        |
+| 0.3287 | 21000  | 0.6353        |
+| 0.3303 | 21100  | 0.6617        |
+| 0.3318 | 21200  | 0.6867        |
+| 0.3334 | 21300  | 0.6681        |
+| 0.3350 | 21400  | 0.7481        |
+| 0.3365 | 21500  | 0.7222        |
+| 0.3381 | 21600  | 0.6653        |
+| 0.3397 | 21700  | 0.6456        |
+| 0.3412 | 21800  | 0.6151        |
+| 0.3428 | 21900  | 0.7371        |
+| 0.3443 | 22000  | 0.6578        |
+| 0.3459 | 22100  | 0.7081        |
+| 0.3475 | 22200  | 0.7069        |
+| 0.3490 | 22300  | 0.762         |
+| 0.3506 | 22400  | 0.7186        |
+| 0.3522 | 22500  | 0.7228        |
+| 0.3537 | 22600  | 0.6919        |
+| 0.3553 | 22700  | 0.7675        |
+| 0.3569 | 22800  | 0.7585        |
+| 0.3584 | 22900  | 0.7495        |
+| 0.3600 | 23000  | 0.7106        |
+| 0.3616 | 23100  | 0.7957        |
+| 0.3631 | 23200  | 0.7996        |
+| 0.3647 | 23300  | 0.6807        |
+| 0.3663 | 23400  | 0.8421        |
+| 0.3678 | 23500  | 0.7041        |
+| 0.3694 | 23600  | 0.77          |
+| 0.3710 | 23700  | 0.8124        |
+| 0.3725 | 23800  | 0.6941        |
+| 0.3741 | 23900  | 0.8293        |
+| 0.3757 | 24000  | 0.8839        |
+| 0.3772 | 24100  | 0.8151        |
+| 0.3788 | 24200  | 0.6954        |
+| 0.3803 | 24300  | 0.7875        |
+| 0.3819 | 24400  | 0.6579        |
+| 0.3835 | 24500  | 0.4184        |
+| 0.3850 | 24600  | 0.53          |
+| 0.3866 | 24700  | 0.4804        |
+| 0.3882 | 24800  | 0.5016        |
+| 0.3897 | 24900  | 0.5219        |
+| 0.3913 | 25000  | 0.4937        |
+| 0.3929 | 25100  | 0.4647        |
+| 0.3944 | 25200  | 0.46          |
+| 0.3960 | 25300  | 0.4756        |
+| 0.3976 | 25400  | 0.4927        |
+| 0.3991 | 25500  | 0.5323        |
+| 0.4007 | 25600  | 0.462         |
+| 0.4023 | 25700  | 0.4368        |
+| 0.4038 | 25800  | 0.3867        |
+| 0.4054 | 25900  | 0.4456        |
+| 0.4070 | 26000  | 0.4454        |
+| 0.4085 | 26100  | 0.4273        |
+| 0.4101 | 26200  | 0.4637        |
+| 0.4116 | 26300  | 0.4516        |
+| 0.4132 | 26400  | 0.436         |
+| 0.4148 | 26500  | 0.4037        |
+| 0.4163 | 26600  | 0.4256        |
+| 0.4179 | 26700  | 0.4481        |
+| 0.4195 | 26800  | 0.4254        |
+| 0.4210 | 26900  | 0.4279        |
+| 0.4226 | 27000  | 0.4248        |
+| 0.4242 | 27100  | 0.4581        |
+| 0.4257 | 27200  | 0.4537        |
+| 0.4273 | 27300  | 0.4178        |
+| 0.4289 | 27400  | 0.441         |
+| 0.4304 | 27500  | 0.5254        |
+| 0.4320 | 27600  | 0.3648        |
+| 0.4336 | 27700  | 0.4023        |
+| 0.4351 | 27800  | 0.4406        |
+| 0.4367 | 27900  | 0.4055        |
+| 0.4383 | 28000  | 0.3305        |
+| 0.4398 | 28100  | 0.3733        |
+| 0.4414 | 28200  | 0.3679        |
+| 0.4430 | 28300  | 0.3942        |
+| 0.4445 | 28400  | 0.4282        |
+| 0.4461 | 28500  | 0.3995        |
+| 0.4476 | 28600  | 0.3282        |
+| 0.4492 | 28700  | 0.3822        |
+| 0.4508 | 28800  | 0.3991        |
+| 0.4523 | 28900  | 0.4001        |
+| 0.4539 | 29000  | 0.4485        |
+| 0.4555 | 29100  | 0.3787        |
+| 0.4570 | 29200  | 0.4055        |
+| 0.4586 | 29300  | 0.4274        |
+| 0.4602 | 29400  | 0.4106        |
+| 0.4617 | 29500  | 0.3746        |
+| 0.4633 | 29600  | 0.3768        |
+| 0.4649 | 29700  | 0.3591        |
+| 0.4664 | 29800  | 0.395         |
+| 0.4680 | 29900  | 0.3783        |
+| 0.4696 | 30000  | 0.3932        |
+| 0.4711 | 30100  | 0.4186        |
+| 0.4727 | 30200  | 0.3538        |
+| 0.4743 | 30300  | 0.3589        |
+| 0.4758 | 30400  | 0.4194        |
+| 0.4774 | 30500  | 0.3879        |
+| 0.4790 | 30600  | 0.3437        |
+| 0.4805 | 30700  | 0.3932        |
+| 0.4821 | 30800  | 0.3417        |
+| 0.4836 | 30900  | 0.3534        |
+| 0.4852 | 31000  | 0.2998        |
+| 0.4868 | 31100  | 0.4275        |
+| 0.4883 | 31200  | 0.3398        |
+| 0.4899 | 31300  | 0.3497        |
+| 0.4915 | 31400  | 0.3066        |
+| 0.4930 | 31500  | 0.3555        |
+| 0.4946 | 31600  | 0.3519        |
+| 0.4962 | 31700  | 0.3386        |
+| 0.4977 | 31800  | 0.3326        |
+| 0.4993 | 31900  | 0.3176        |
+| 0.5009 | 32000  | 0.3464        |
+| 0.5024 | 32100  | 0.3588        |
+| 0.5040 | 32200  | 0.3656        |
+| 0.5056 | 32300  | 0.3168        |
+| 0.5071 | 32400  | 0.3859        |
+| 0.5087 | 32500  | 0.3668        |
+| 0.5103 | 32600  | 0.3125        |
+| 0.5118 | 32700  | 0.3357        |
+| 0.5134 | 32800  | 0.3328        |
+| 0.5150 | 32900  | 0.3245        |
+| 0.5165 | 33000  | 0.3408        |
+| 0.5181 | 33100  | 0.3848        |
+| 0.5196 | 33200  | 0.3401        |
+| 0.5212 | 33300  | 0.2744        |
+| 0.5228 | 33400  | 0.3138        |
+| 0.5243 | 33500  | 0.2953        |
+| 0.5259 | 33600  | 0.2965        |
+| 0.5275 | 33700  | 0.2972        |
+| 0.5290 | 33800  | 0.3247        |
+| 0.5306 | 33900  | 0.3158        |
+| 0.5322 | 34000  | 0.3184        |
+| 0.5337 | 34100  | 0.3292        |
+| 0.5353 | 34200  | 0.2914        |
+| 0.5369 | 34300  | 0.3536        |
+| 0.5384 | 34400  | 0.285         |
+| 0.5400 | 34500  | 0.3322        |
+| 0.5416 | 34600  | 0.3349        |
+| 0.5431 | 34700  | 0.3244        |
+| 0.5447 | 34800  | 0.253         |
+| 0.5463 | 34900  | 0.314         |
+| 0.5478 | 35000  | 0.3751        |
+| 0.5494 | 35100  | 0.2968        |
+| 0.5510 | 35200  | 0.3863        |
+| 0.5525 | 35300  | 0.2914        |
+| 0.5541 | 35400  | 0.2906        |
+| 0.5556 | 35500  | 0.3472        |
+| 0.5572 | 35600  | 0.3088        |
+| 0.5588 | 35700  | 0.3016        |
+| 0.5603 | 35800  | 0.3584        |
+| 0.5619 | 35900  | 0.3282        |
+| 0.5635 | 36000  | 0.4005        |
+| 0.5650 | 36100  | 0.3266        |
+| 0.5666 | 36200  | 0.3704        |
+| 0.5682 | 36300  | 0.4014        |
+| 0.5697 | 36400  | 0.3866        |
+| 0.5713 | 36500  | 0.3927        |
+| 0.5729 | 36600  | 0.3595        |
+| 0.5744 | 36700  | 0.3386        |
+| 0.5760 | 36800  | 0.394         |
+| 0.5776 | 36900  | 0.4363        |
+| 0.5791 | 37000  | 0.4669        |
+| 0.5807 | 37100  | 0.4404        |
+| 0.5823 | 37200  | 0.4326        |
+| 0.5838 | 37300  | 0.4303        |
+| 0.5854 | 37400  | 0.4496        |
+| 0.5870 | 37500  | 0.4461        |
+| 0.5885 | 37600  | 0.5314        |
+| 0.5901 | 37700  | 0.5424        |
+| 0.5916 | 37800  | 0.4604        |
+| 0.5932 | 37900  | 0.515         |
+| 0.5948 | 38000  | 0.5045        |
+| 0.5963 | 38100  | 0.5254        |
+| 0.5979 | 38200  | 0.5213        |
+| 0.5995 | 38300  | 0.5704        |
+| 0.6010 | 38400  | 0.5427        |
+| 0.6026 | 38500  | 0.4767        |
+| 0.6042 | 38600  | 0.5317        |
+| 0.6057 | 38700  | 0.5019        |
+| 0.6073 | 38800  | 0.5453        |
+| 0.6089 | 38900  | 0.5469        |
+| 0.6104 | 39000  | 0.4875        |
+| 0.6120 | 39100  | 0.5239        |
+| 0.6136 | 39200  | 0.5179        |
+| 0.6151 | 39300  | 0.5316        |
+| 0.6167 | 39400  | 0.523         |
+| 0.6183 | 39500  | 0.5474        |
+| 0.6198 | 39600  | 0.5844        |
+| 0.6214 | 39700  | 0.5094        |
+| 0.6230 | 39800  | 0.5815        |
+| 0.6245 | 39900  | 0.508         |
+| 0.6261 | 40000  | 0.4752        |
+| 0.6276 | 40100  | 0.5505        |
+| 0.6292 | 40200  | 0.4832        |
+| 0.6308 | 40300  | 0.5106        |
+| 0.6323 | 40400  | 0.556         |
+| 0.6339 | 40500  | 0.522         |
+| 0.6355 | 40600  | 0.5709        |
+| 0.6370 | 40700  | 0.521         |
+| 0.6386 | 40800  | 0.4999        |
+| 0.6402 | 40900  | 0.5338        |
+| 0.6417 | 41000  | 0.5275        |
+| 0.6433 | 41100  | 0.4885        |
+| 0.6449 | 41200  | 0.4608        |
+| 0.6464 | 41300  | 0.5604        |
+| 0.6480 | 41400  | 0.4158        |
+| 0.6496 | 41500  | 0.5148        |
+| 0.6511 | 41600  | 0.4784        |
+| 0.6527 | 41700  | 0.4744        |
+| 0.6543 | 41800  | 0.4993        |
+| 0.6558 | 41900  | 0.4616        |
+| 0.6574 | 42000  | 0.4763        |
+| 0.6590 | 42100  | 0.4979        |
+| 0.6605 | 42200  | 0.4679        |
+| 0.6621 | 42300  | 0.4349        |
+| 0.6636 | 42400  | 0.4849        |
+| 0.6652 | 42500  | 0.487         |
+| 0.6668 | 42600  | 0.4632        |
+| 0.6683 | 42700  | 0.4418        |
+| 0.6699 | 42800  | 0.4591        |
+| 0.6715 | 42900  | 0.473         |
+| 0.6730 | 43000  | 0.4695        |
+| 0.6746 | 43100  | 0.4785        |
+| 0.6762 | 43200  | 0.4614        |
+| 0.6777 | 43300  | 0.5182        |
+| 0.6793 | 43400  | 0.4268        |
+| 0.6809 | 43500  | 0.4301        |
+| 0.6824 | 43600  | 0.3894        |
+| 0.6840 | 43700  | 0.4174        |
+| 0.6856 | 43800  | 0.4129        |
+| 0.6871 | 43900  | 0.3985        |
+| 0.6887 | 44000  | 0.4547        |
+| 0.6903 | 44100  | 0.4121        |
+| 0.6918 | 44200  | 0.4345        |
+| 0.6934 | 44300  | 0.3525        |
+| 0.6950 | 44400  | 0.3674        |
+| 0.6965 | 44500  | 0.4406        |
+| 0.6981 | 44600  | 0.4281        |
+| 0.6996 | 44700  | 0.4201        |
+| 0.7012 | 44800  | 0.4308        |
+| 0.7028 | 44900  | 0.4303        |
+| 0.7043 | 45000  | 0.4358        |
+| 0.7059 | 45100  | 0.3965        |
+| 0.7075 | 45200  | 0.4004        |
+| 0.7090 | 45300  | 0.422         |
+| 0.7106 | 45400  | 0.4235        |
+| 0.7122 | 45500  | 0.3864        |
+| 0.7137 | 45600  | 0.3423        |
+| 0.7153 | 45700  | 0.3983        |
+| 0.7169 | 45800  | 0.3423        |
+| 0.7184 | 45900  | 0.3757        |
+| 0.7200 | 46000  | 0.4296        |
+| 0.7216 | 46100  | 0.3518        |
+| 0.7231 | 46200  | 0.3589        |
+| 0.7247 | 46300  | 0.3653        |
+| 0.7263 | 46400  | 0.3881        |
+| 0.7278 | 46500  | 0.3762        |
+| 0.7294 | 46600  | 0.3941        |
+| 0.7310 | 46700  | 0.3596        |
+| 0.7325 | 46800  | 0.323         |
+| 0.7341 | 46900  | 0.3331        |
+| 0.7356 | 47000  | 0.3551        |
+| 0.7372 | 47100  | 0.3599        |
+| 0.7388 | 47200  | 0.3255        |
+| 0.7403 | 47300  | 0.2938        |
+| 0.7419 | 47400  | 0.3351        |
+| 0.7435 | 47500  | 0.341         |
+| 0.7450 | 47600  | 0.3388        |
+| 0.7466 | 47700  | 0.325         |
+| 0.7482 | 47800  | 0.3545        |
+| 0.7497 | 47900  | 0.3068        |
+| 0.7513 | 48000  | 0.29          |
+| 0.7529 | 48100  | 0.3051        |
+| 0.7544 | 48200  | 0.311         |
+| 0.7560 | 48300  | 0.3396        |
+| 0.7576 | 48400  | 0.3347        |
+| 0.7591 | 48500  | 0.3219        |
+| 0.7607 | 48600  | 0.2916        |
+| 0.7623 | 48700  | 0.2772        |
+| 0.7638 | 48800  | 0.3239        |
+| 0.7654 | 48900  | 0.3208        |
+| 0.7670 | 49000  | 0.3585        |
+| 0.7685 | 49100  | 0.3219        |
+| 0.7701 | 49200  | 0.3573        |
+| 0.7716 | 49300  | 0.2854        |
+| 0.7732 | 49400  | 0.3193        |
+| 0.7748 | 49500  | 0.3109        |
+| 0.7763 | 49600  | 0.2972        |
+| 0.7779 | 49700  | 0.3188        |
+| 0.7795 | 49800  | 0.3122        |
+| 0.7810 | 49900  | 0.2882        |
+| 0.7826 | 50000  | 0.3077        |
+| 0.7842 | 50100  | 0.2796        |
+| 0.7857 | 50200  | 0.3187        |
+| 0.7873 | 50300  | 0.3329        |
+| 0.7889 | 50400  | 0.3291        |
+| 0.7904 | 50500  | 0.3153        |
+| 0.7920 | 50600  | 0.3092        |
+| 0.7936 | 50700  | 0.2549        |
+| 0.7951 | 50800  | 0.2795        |
+| 0.7967 | 50900  | 0.2955        |
+| 0.7983 | 51000  | 0.362         |
+| 0.7998 | 51100  | 0.2585        |
+| 0.8014 | 51200  | 0.2437        |
+| 0.8030 | 51300  | 0.291         |
+| 0.8045 | 51400  | 0.2639        |
+| 0.8061 | 51500  | 0.2785        |
+| 0.8076 | 51600  | 0.2739        |
+| 0.8092 | 51700  | 0.2699        |
+| 0.8108 | 51800  | 0.3007        |
+| 0.8123 | 51900  | 0.3044        |
+| 0.8139 | 52000  | 0.2994        |
+| 0.8155 | 52100  | 0.2742        |
+| 0.8170 | 52200  | 0.291         |
+| 0.8186 | 52300  | 0.2517        |
+| 0.8202 | 52400  | 0.2613        |
+| 0.8217 | 52500  | 0.2767        |
+| 0.8233 | 52600  | 0.2424        |
+| 0.8249 | 52700  | 0.2666        |
+| 0.8264 | 52800  | 0.262         |
+| 0.8280 | 52900  | 0.2884        |
+| 0.8296 | 53000  | 0.2636        |
+| 0.8311 | 53100  | 0.2807        |
+| 0.8327 | 53200  | 0.2737        |
+| 0.8343 | 53300  | 0.2764        |
+| 0.8358 | 53400  | 0.2652        |
+| 0.8374 | 53500  | 0.3061        |
+| 0.8390 | 53600  | 0.2704        |
+| 0.8405 | 53700  | 0.2372        |
+| 0.8421 | 53800  | 0.2595        |
+| 0.8436 | 53900  | 0.2662        |
+| 0.8452 | 54000  | 0.2692        |
+| 0.8468 | 54100  | 0.246         |
+| 0.8483 | 54200  | 0.2571        |
+| 0.8499 | 54300  | 0.2485        |
+| 0.8515 | 54400  | 0.2418        |
+| 0.8530 | 54500  | 0.3039        |
+| 0.8546 | 54600  | 0.2218        |
+| 0.8562 | 54700  | 0.2676        |
+| 0.8577 | 54800  | 0.2299        |
+| 0.8593 | 54900  | 0.2782        |
+| 0.8609 | 55000  | 0.2779        |
+| 0.8624 | 55100  | 0.2817        |
+| 0.8640 | 55200  | 0.2549        |
+| 0.8656 | 55300  | 0.2361        |
+| 0.8671 | 55400  | 0.2599        |
+| 0.8687 | 55500  | 0.231         |
+| 0.8703 | 55600  | 0.2741        |
+| 0.8718 | 55700  | 0.2553        |
+| 0.8734 | 55800  | 0.2569        |
+| 0.8750 | 55900  | 0.2338        |
+| 0.8765 | 56000  | 0.2212        |
+| 0.8781 | 56100  | 0.2301        |
+| 0.8796 | 56200  | 0.2518        |
+| 0.8812 | 56300  | 0.2485        |
+| 0.8828 | 56400  | 0.2373        |
+| 0.8843 | 56500  | 0.2346        |
+| 0.8859 | 56600  | 0.249         |
+| 0.8875 | 56700  | 0.2295        |
+| 0.8890 | 56800  | 0.2208        |
+| 0.8906 | 56900  | 0.2356        |
+| 0.8922 | 57000  | 0.2405        |
+| 0.8937 | 57100  | 0.2211        |
+| 0.8953 | 57200  | 0.2641        |
+| 0.8969 | 57300  | 0.2104        |
+| 0.8984 | 57400  | 0.2586        |
+| 0.9000 | 57500  | 0.2369        |
+| 0.9016 | 57600  | 0.2396        |
+| 0.9031 | 57700  | 0.2014        |
+| 0.9047 | 57800  | 0.2532        |
+| 0.9063 | 57900  | 0.2141        |
+| 0.9078 | 58000  | 0.232         |
+| 0.9094 | 58100  | 0.2189        |
+| 0.9110 | 58200  | 0.2174        |
+| 0.9125 | 58300  | 0.1974        |
+| 0.9141 | 58400  | 0.2119        |
+| 0.9156 | 58500  | 0.2294        |
+| 0.9172 | 58600  | 0.2379        |
+| 0.9188 | 58700  | 0.1962        |
+| 0.9203 | 58800  | 0.2299        |
+| 0.9219 | 58900  | 0.2104        |
+| 0.9235 | 59000  | 0.2229        |
+| 0.9250 | 59100  | 0.204         |
+| 0.9266 | 59200  | 0.1816        |
+| 0.9282 | 59300  | 0.2173        |
+| 0.9297 | 59400  | 0.2037        |
+| 0.9313 | 59500  | 0.2005        |
+| 0.9329 | 59600  | 0.1998        |
+| 0.9344 | 59700  | 0.1918        |
+| 0.9360 | 59800  | 0.2022        |
+| 0.9376 | 59900  | 0.1858        |
+| 0.9391 | 60000  | 0.2084        |
+| 0.9407 | 60100  | 0.1984        |
+| 0.9423 | 60200  | 0.2009        |
+| 0.9438 | 60300  | 0.1694        |
+| 0.9454 | 60400  | 0.2507        |
+| 0.9470 | 60500  | 0.2082        |
+| 0.9485 | 60600  | 0.1805        |
+| 0.9501 | 60700  | 0.2002        |
+| 0.9516 | 60800  | 0.2165        |
+| 0.9532 | 60900  | 0.2232        |
+| 0.9548 | 61000  | 0.1963        |
+| 0.9563 | 61100  | 0.165         |
+| 0.9579 | 61200  | 0.1947        |
+| 0.9595 | 61300  | 0.2308        |
+| 0.9610 | 61400  | 0.1987        |
+| 0.9626 | 61500  | 0.2113        |
+| 0.9642 | 61600  | 0.2413        |
+| 0.9657 | 61700  | 0.2001        |
+| 0.9673 | 61800  | 0.2219        |
+| 0.9689 | 61900  | 0.2279        |
+| 0.9704 | 62000  | 0.2258        |
+| 0.9720 | 62100  | 0.1654        |
+| 0.9736 | 62200  | 0.1555        |
+| 0.9751 | 62300  | 0.1716        |
+| 0.9767 | 62400  | 0.1832        |
+| 0.9783 | 62500  | 0.1905        |
+| 0.9798 | 62600  | 0.1859        |
+| 0.9814 | 62700  | 0.1681        |
+| 0.9830 | 62800  | 0.1811        |
+| 0.9845 | 62900  | 0.2062        |
+| 0.9861 | 63000  | 0.1769        |
+| 0.9876 | 63100  | 0.1367        |
+| 0.9892 | 63200  | 0.1801        |
+| 0.9908 | 63300  | 0.1386        |
+| 0.9923 | 63400  | 0.1989        |
+| 0.9939 | 63500  | 0.1574        |
+| 0.9955 | 63600  | 0.1584        |
+| 0.9970 | 63700  | 0.2672        |
+| 0.9986 | 63800  | 0.3305        |
+| 1.0002 | 63900  | 0.3934        |
+| 1.0018 | 64000  | 0.061         |
+| 1.0033 | 64100  | 0.0605        |
+| 1.0049 | 64200  | 0.0466        |
+| 1.0064 | 64300  | 0.0626        |
+| 1.0080 | 64400  | 0.0861        |
+| 1.0096 | 64500  | 0.0991        |
+| 1.0111 | 64600  | 0.2984        |
+| 1.0127 | 64700  | 0.5773        |
+| 1.0143 | 64800  | 0.9207        |
+| 1.0158 | 64900  | 0.5435        |
+| 1.0174 | 65000  | 0.6465        |
+| 1.0190 | 65100  | 0.6672        |
+| 1.0205 | 65200  | 0.6251        |
+| 1.0221 | 65300  | 0.6118        |
+| 1.0237 | 65400  | 0.4914        |
+| 1.0252 | 65500  | 0.5557        |
+| 1.0268 | 65600  | 0.6045        |
+| 1.0284 | 65700  | 0.5637        |
+| 1.0299 | 65800  | 0.8428        |
+| 1.0315 | 65900  | 0.6716        |
+| 1.0331 | 66000  | 0.6027        |
+| 1.0346 | 66100  | 0.6518        |
+| 1.0362 | 66200  | 0.5806        |
+| 1.0378 | 66300  | 0.5392        |
+| 1.0393 | 66400  | 0.5913        |
+| 1.0409 | 66500  | 0.5733        |
+| 1.0424 | 66600  | 0.604         |
+| 1.0440 | 66700  | 0.5877        |
+| 1.0456 | 66800  | 0.556         |
+| 1.0471 | 66900  | 0.5371        |
+| 1.0487 | 67000  | 0.5135        |
+| 1.0503 | 67100  | 0.5408        |
+| 1.0518 | 67200  | 0.5689        |
+| 1.0534 | 67300  | 0.5943        |
+| 1.0550 | 67400  | 0.5994        |
+| 1.0565 | 67500  | 0.6756        |
+| 1.0581 | 67600  | 0.625         |
+| 1.0597 | 67700  | 0.6065        |
+| 1.0612 | 67800  | 0.5901        |
+| 1.0628 | 67900  | 0.6384        |
+| 1.0644 | 68000  | 0.6305        |
+| 1.0659 | 68100  | 0.6138        |
+| 1.0675 | 68200  | 0.6068        |
+| 1.0691 | 68300  | 0.6477        |
+| 1.0706 | 68400  | 0.617         |
+| 1.0722 | 68500  | 0.625         |
+| 1.0738 | 68600  | 0.6302        |
+| 1.0753 | 68700  | 0.6513        |
+| 1.0769 | 68800  | 0.6124        |
+| 1.0784 | 68900  | 0.6971        |
+| 1.0800 | 69000  | 0.6763        |
+| 1.0816 | 69100  | 0.6935        |
+| 1.0831 | 69200  | 0.6307        |
+| 1.0847 | 69300  | 0.6509        |
+| 1.0863 | 69400  | 0.6519        |
+| 1.0878 | 69500  | 0.6832        |
+| 1.0894 | 69600  | 0.5655        |
+| 1.0910 | 69700  | 0.6134        |
+| 1.0925 | 69800  | 0.6029        |
+| 1.0941 | 69900  | 0.5779        |
+| 1.0957 | 70000  | 0.6158        |
+| 1.0972 | 70100  | 0.5758        |
+| 1.0988 | 70200  | 0.5649        |
+| 1.1004 | 70300  | 0.5438        |
+| 1.1019 | 70400  | 0.543         |
+| 1.1035 | 70500  | 0.5765        |
+| 1.1051 | 70600  | 0.6113        |
+| 1.1066 | 70700  | 0.5815        |
+| 1.1082 | 70800  | 0.5942        |
+| 1.1098 | 70900  | 0.6293        |
+| 1.1113 | 71000  | 0.5186        |
+| 1.1129 | 71100  | 0.5703        |
+| 1.1144 | 71200  | 0.5688        |
+| 1.1160 | 71300  | 0.5855        |
+| 1.1176 | 71400  | 0.5591        |
+| 1.1191 | 71500  | 0.5137        |
+| 1.1207 | 71600  | 0.5905        |
+| 1.1223 | 71700  | 0.5123        |
+| 1.1238 | 71800  | 0.5028        |
+| 1.1254 | 71900  | 0.5806        |
+| 1.1270 | 72000  | 0.5305        |
+| 1.1285 | 72100  | 0.5299        |
+| 1.1301 | 72200  | 0.5293        |
+| 1.1317 | 72300  | 0.4948        |
+| 1.1332 | 72400  | 0.5292        |
+| 1.1348 | 72500  | 0.5252        |
+| 1.1364 | 72600  | 0.5153        |
+| 1.1379 | 72700  | 0.5695        |
+| 1.1395 | 72800  | 0.5157        |
+| 1.1411 | 72900  | 0.5078        |
+| 1.1426 | 73000  | 0.5311        |
+| 1.1442 | 73100  | 0.4657        |
+| 1.1458 | 73200  | 0.518         |
+| 1.1473 | 73300  | 0.5145        |
+| 1.1489 | 73400  | 0.553         |
+| 1.1504 | 73500  | 0.5048        |
+| 1.1520 | 73600  | 0.4276        |
+| 1.1536 | 73700  | 0.5176        |
+| 1.1551 | 73800  | 0.4791        |
+| 1.1567 | 73900  | 0.4971        |
+| 1.1583 | 74000  | 0.4629        |
+| 1.1598 | 74100  | 0.5753        |
+| 1.1614 | 74200  | 0.5251        |
+| 1.1630 | 74300  | 0.4927        |
+| 1.1645 | 74400  | 0.4722        |
+| 1.1661 | 74500  | 0.4372        |
+| 1.1677 | 74600  | 0.4661        |
+| 1.1692 | 74700  | 0.4696        |
+| 1.1708 | 74800  | 0.4959        |
+| 1.1724 | 74900  | 0.468         |
+| 1.1739 | 75000  | 0.4668        |
+| 1.1755 | 75100  | 0.436         |
+| 1.1771 | 75200  | 0.47          |
+| 1.1786 | 75300  | 0.4695        |
+| 1.1802 | 75400  | 0.4892        |
+| 1.1818 | 75500  | 0.4626        |
+| 1.1833 | 75600  | 0.3783        |
+| 1.1849 | 75700  | 0.4643        |
+| 1.1864 | 75800  | 0.4487        |
+| 1.1880 | 75900  | 0.4633        |
+| 1.1896 | 76000  | 0.5046        |
+| 1.1911 | 76100  | 0.4137        |
+| 1.1927 | 76200  | 0.4798        |
+| 1.1943 | 76300  | 0.4893        |
+| 1.1958 | 76400  | 0.4699        |
+| 1.1974 | 76500  | 0.488         |
+| 1.1990 | 76600  | 0.4606        |
+| 1.2005 | 76700  | 0.5116        |
+| 1.2021 | 76800  | 0.4376        |
+| 1.2037 | 76900  | 0.5005        |
+| 1.2052 | 77000  | 0.4513        |
+| 1.2068 | 77100  | 0.4805        |
+| 1.2084 | 77200  | 0.4339        |
+| 1.2099 | 77300  | 0.464         |
+| 1.2115 | 77400  | 0.4584        |
+| 1.2131 | 77500  | 0.4996        |
+| 1.2146 | 77600  | 0.4658        |
+| 1.2162 | 77700  | 0.4269        |
+| 1.2178 | 77800  | 0.4783        |
+| 1.2193 | 77900  | 0.4737        |
+| 1.2209 | 78000  | 0.4465        |
+| 1.2224 | 78100  | 0.4581        |
+| 1.2240 | 78200  | 0.4007        |
+| 1.2256 | 78300  | 0.5317        |
+| 1.2271 | 78400  | 0.4474        |
+| 1.2287 | 78500  | 0.4715        |
+| 1.2303 | 78600  | 0.5003        |
+| 1.2318 | 78700  | 0.4596        |
+| 1.2334 | 78800  | 0.4475        |
+| 1.2350 | 78900  | 0.3714        |
+| 1.2365 | 79000  | 0.4179        |
+| 1.2381 | 79100  | 0.4371        |
+| 1.2397 | 79200  | 0.4772        |
+| 1.2412 | 79300  | 0.4611        |
+| 1.2428 | 79400  | 0.4518        |
+| 1.2444 | 79500  | 0.5327        |
+| 1.2459 | 79600  | 0.4819        |
+| 1.2475 | 79700  | 0.4928        |
+| 1.2491 | 79800  | 0.5269        |
+| 1.2506 | 79900  | 0.4739        |
+| 1.2522 | 80000  | 0.5247        |
+| 1.2538 | 80100  | 0.4922        |
+| 1.2553 | 80200  | 0.499         |
+| 1.2569 | 80300  | 0.4879        |
+| 1.2584 | 80400  | 0.4798        |
+| 1.2600 | 80500  | 0.4917        |
+| 1.2616 | 80600  | 0.4719        |
+| 1.2631 | 80700  | 0.4937        |
+| 1.2647 | 80800  | 0.5218        |
+| 1.2663 | 80900  | 0.4716        |
+| 1.2678 | 81000  | 0.4111        |
+| 1.2694 | 81100  | 0.4639        |
+| 1.2710 | 81200  | 0.4828        |
+| 1.2725 | 81300  | 0.4947        |
+| 1.2741 | 81400  | 0.5332        |
+| 1.2757 | 81500  | 0.4903        |
+| 1.2772 | 81600  | 0.5018        |
+| 1.2788 | 81700  | 0.4993        |
+| 1.2804 | 81800  | 0.4921        |
+| 1.2819 | 81900  | 0.4922        |
+| 1.2835 | 82000  | 0.5072        |
+| 1.2851 | 82100  | 0.4958        |
+| 1.2866 | 82200  | 0.4452        |
+| 1.2882 | 82300  | 0.5346        |
+| 1.2898 | 82400  | 0.4844        |
+| 1.2913 | 82500  | 0.4459        |
+| 1.2929 | 82600  | 0.5695        |
+| 1.2944 | 82700  | 0.5381        |
+| 1.2960 | 82800  | 0.5174        |
+| 1.2976 | 82900  | 0.4948        |
+| 1.2991 | 83000  | 0.5166        |
+| 1.3007 | 83100  | 0.5101        |
+| 1.3023 | 83200  | 0.5102        |
+| 1.3038 | 83300  | 0.5428        |
+| 1.3054 | 83400  | 0.4097        |
+| 1.3070 | 83500  | 0.4566        |
+| 1.3085 | 83600  | 0.4987        |
+| 1.3101 | 83700  | 0.4754        |
+| 1.3117 | 83800  | 0.5283        |
+| 1.3132 | 83900  | 0.4426        |
+| 1.3148 | 84000  | 0.4723        |
+| 1.3164 | 84100  | 0.4705        |
+| 1.3179 | 84200  | 0.4368        |
+| 1.3195 | 84300  | 0.4495        |
+| 1.3211 | 84400  | 0.5593        |
+| 1.3226 | 84500  | 0.4466        |
+| 1.3242 | 84600  | 0.4994        |
+| 1.3258 | 84700  | 0.456         |
+| 1.3273 | 84800  | 0.4788        |
+| 1.3289 | 84900  | 0.4185        |
+| 1.3304 | 85000  | 0.4321        |
+| 1.3320 | 85100  | 0.4796        |
+| 1.3336 | 85200  | 0.4207        |
+| 1.3351 | 85300  | 0.4875        |
+| 1.3367 | 85400  | 0.5018        |
+| 1.3383 | 85500  | 0.4184        |
+| 1.3398 | 85600  | 0.4233        |
+| 1.3414 | 85700  | 0.423         |
+| 1.3430 | 85800  | 0.4756        |
+| 1.3445 | 85900  | 0.4477        |
+| 1.3461 | 86000  | 0.4468        |
+| 1.3477 | 86100  | 0.49          |
+| 1.3492 | 86200  | 0.481         |
+| 1.3508 | 86300  | 0.4905        |
+| 1.3524 | 86400  | 0.4642        |
+| 1.3539 | 86500  | 0.4864        |
+| 1.3555 | 86600  | 0.4776        |
+| 1.3571 | 86700  | 0.5025        |
+| 1.3586 | 86800  | 0.5197        |
+| 1.3602 | 86900  | 0.4791        |
+| 1.3618 | 87000  | 0.5563        |
+| 1.3633 | 87100  | 0.5164        |
+| 1.3649 | 87200  | 0.4704        |
+| 1.3664 | 87300  | 0.5112        |
+| 1.3680 | 87400  | 0.4766        |
+| 1.3696 | 87500  | 0.47          |
+| 1.3711 | 87600  | 0.5587        |
+| 1.3727 | 87700  | 0.521         |
+| 1.3743 | 87800  | 0.5563        |
+| 1.3758 | 87900  | 0.5557        |
+| 1.3774 | 88000  | 0.5995        |
+| 1.3790 | 88100  | 0.4425        |
+| 1.3805 | 88200  | 0.5123        |
+| 1.3821 | 88300  | 0.3313        |
+| 1.3837 | 88400  | 0.2502        |
+| 1.3852 | 88500  | 0.3148        |
+| 1.3868 | 88600  | 0.2991        |
+| 1.3884 | 88700  | 0.2907        |
+| 1.3899 | 88800  | 0.3261        |
+| 1.3915 | 88900  | 0.2762        |
+| 1.3931 | 89000  | 0.2481        |
+| 1.3946 | 89100  | 0.2885        |
+| 1.3962 | 89200  | 0.285         |
+| 1.3978 | 89300  | 0.3068        |
+| 1.3993 | 89400  | 0.3083        |
+| 1.4009 | 89500  | 0.2803        |
+| 1.4024 | 89600  | 0.2403        |
+| 1.4040 | 89700  | 0.236         |
+| 1.4056 | 89800  | 0.2668        |
+| 1.4071 | 89900  | 0.2458        |
+| 1.4087 | 90000  | 0.233         |
+| 1.4103 | 90100  | 0.2855        |
+| 1.4118 | 90200  | 0.2446        |
+| 1.4134 | 90300  | 0.2402        |
+| 1.4150 | 90400  | 0.2284        |
+| 1.4165 | 90500  | 0.2357        |
+| 1.4181 | 90600  | 0.2682        |
+| 1.4197 | 90700  | 0.2467        |
+| 1.4212 | 90800  | 0.2344        |
+| 1.4228 | 90900  | 0.2502        |
+| 1.4244 | 91000  | 0.2802        |
+| 1.4259 | 91100  | 0.2516        |
+| 1.4275 | 91200  | 0.239         |
+| 1.4291 | 91300  | 0.2688        |
+| 1.4306 | 91400  | 0.3018        |
+| 1.4322 | 91500  | 0.2068        |
+| 1.4338 | 91600  | 0.237         |
+| 1.4353 | 91700  | 0.2706        |
+| 1.4369 | 91800  | 0.2063        |
+| 1.4384 | 91900  | 0.2011        |
+| 1.4400 | 92000  | 0.1828        |
+| 1.4416 | 92100  | 0.2143        |
+| 1.4431 | 92200  | 0.204         |
+| 1.4447 | 92300  | 0.287         |
+| 1.4463 | 92400  | 0.2023        |
+| 1.4478 | 92500  | 0.1836        |
+| 1.4494 | 92600  | 0.2298        |
+| 1.4510 | 92700  | 0.2276        |
+| 1.4525 | 92800  | 0.2091        |
+| 1.4541 | 92900  | 0.2535        |
+| 1.4557 | 93000  | 0.2091        |
+| 1.4572 | 93100  | 0.2232        |
+| 1.4588 | 93200  | 0.2334        |
+| 1.4604 | 93300  | 0.2396        |
+| 1.4619 | 93400  | 0.2397        |
+| 1.4635 | 93500  | 0.2211        |
+| 1.4651 | 93600  | 0.1989        |
+| 1.4666 | 93700  | 0.2416        |
+| 1.4682 | 93800  | 0.2343        |
+| 1.4698 | 93900  | 0.2134        |
+| 1.4713 | 94000  | 0.218         |
+| 1.4729 | 94100  | 0.2056        |
+| 1.4744 | 94200  | 0.193         |
+| 1.4760 | 94300  | 0.2516        |
+| 1.4776 | 94400  | 0.2003        |
+| 1.4791 | 94500  | 0.1954        |
+| 1.4807 | 94600  | 0.2076        |
+| 1.4823 | 94700  | 0.1803        |
+| 1.4838 | 94800  | 0.2114        |
+| 1.4854 | 94900  | 0.1694        |
+| 1.4870 | 95000  | 0.2608        |
+| 1.4885 | 95100  | 0.1988        |
+| 1.4901 | 95200  | 0.2171        |
+| 1.4917 | 95300  | 0.1767        |
+| 1.4932 | 95400  | 0.1929        |
+| 1.4948 | 95500  | 0.2025        |
+| 1.4964 | 95600  | 0.1919        |
+| 1.4979 | 95700  | 0.1798        |
+| 1.4995 | 95800  | 0.1656        |
+| 1.5011 | 95900  | 0.1985        |
+| 1.5026 | 96000  | 0.2399        |
+| 1.5042 | 96100  | 0.1773        |
+| 1.5058 | 96200  | 0.1985        |
+| 1.5073 | 96300  | 0.1957        |
+| 1.5089 | 96400  | 0.2185        |
+| 1.5104 | 96500  | 0.178         |
+| 1.5120 | 96600  | 0.1994        |
+| 1.5136 | 96700  | 0.1834        |
+| 1.5151 | 96800  | 0.1804        |
+| 1.5167 | 96900  | 0.1966        |
+| 1.5183 | 97000  | 0.2043        |
+| 1.5198 | 97100  | 0.2032        |
+| 1.5214 | 97200  | 0.1559        |
+| 1.5230 | 97300  | 0.1827        |
+| 1.5245 | 97400  | 0.1628        |
+| 1.5261 | 97500  | 0.1637        |
+| 1.5277 | 97600  | 0.1795        |
+| 1.5292 | 97700  | 0.1775        |
+| 1.5308 | 97800  | 0.178         |
+| 1.5324 | 97900  | 0.1749        |
+| 1.5339 | 98000  | 0.1894        |
+| 1.5355 | 98100  | 0.1594        |
+| 1.5371 | 98200  | 0.1879        |
+| 1.5386 | 98300  | 0.1657        |
+| 1.5402 | 98400  | 0.173         |
+| 1.5417 | 98500  | 0.1869        |
+| 1.5433 | 98600  | 0.1754        |
+| 1.5449 | 98700  | 0.1262        |
+| 1.5464 | 98800  | 0.1721        |
+| 1.5480 | 98900  | 0.194         |
+| 1.5496 | 99000  | 0.1595        |
+| 1.5511 | 99100  | 0.1991        |
+| 1.5527 | 99200  | 0.1499        |
+| 1.5543 | 99300  | 0.1455        |
+| 1.5558 | 99400  | 0.1935        |
+| 1.5574 | 99500  | 0.1716        |
+| 1.5590 | 99600  | 0.1654        |
+| 1.5605 | 99700  | 0.1993        |
+| 1.5621 | 99800  | 0.1828        |
+| 1.5637 | 99900  | 0.2098        |
+| 1.5652 | 100000 | 0.1746        |
+| 1.5668 | 100100 | 0.2337        |
+| 1.5684 | 100200 | 0.2331        |
+| 1.5699 | 100300 | 0.2213        |
+| 1.5715 | 100400 | 0.2236        |
+| 1.5731 | 100500 | 0.1764        |
+| 1.5746 | 100600 | 0.1885        |
+| 1.5762 | 100700 | 0.2246        |
+| 1.5777 | 100800 | 0.263         |
+| 1.5793 | 100900 | 0.2725        |
+| 1.5809 | 101000 | 0.233         |
+| 1.5824 | 101100 | 0.2646        |
+| 1.5840 | 101200 | 0.2527        |
+| 1.5856 | 101300 | 0.2593        |
+| 1.5871 | 101400 | 0.2511        |
+| 1.5887 | 101500 | 0.3076        |
+| 1.5903 | 101600 | 0.2993        |
+| 1.5918 | 101700 | 0.2508        |
+| 1.5934 | 101800 | 0.3101        |
+| 1.5950 | 101900 | 0.2966        |
+| 1.5965 | 102000 | 0.2877        |
+| 1.5981 | 102100 | 0.3309        |
+| 1.5997 | 102200 | 0.3473        |
+| 1.6012 | 102300 | 0.3053        |
+| 1.6028 | 102400 | 0.2778        |
+| 1.6044 | 102500 | 0.31          |
+| 1.6059 | 102600 | 0.2798        |
+| 1.6075 | 102700 | 0.3022        |
+| 1.6091 | 102800 | 0.2979        |
+| 1.6106 | 102900 | 0.3125        |
+| 1.6122 | 103000 | 0.2893        |
+| 1.6137 | 103100 | 0.3125        |
+| 1.6153 | 103200 | 0.3033        |
+| 1.6169 | 103300 | 0.3172        |
+| 1.6184 | 103400 | 0.3001        |
+| 1.6200 | 103500 | 0.3095        |
+| 1.6216 | 103600 | 0.3096        |
+| 1.6231 | 103700 | 0.356         |
+| 1.6247 | 103800 | 0.3126        |
+| 1.6263 | 103900 | 0.2989        |
+| 1.6278 | 104000 | 0.3144        |
+| 1.6294 | 104100 | 0.2929        |
+| 1.6310 | 104200 | 0.2893        |
+| 1.6325 | 104300 | 0.3429        |
+| 1.6341 | 104400 | 0.3013        |
+| 1.6357 | 104500 | 0.3501        |
+| 1.6372 | 104600 | 0.2902        |
+| 1.6388 | 104700 | 0.3155        |
+| 1.6404 | 104800 | 0.3129        |
+| 1.6419 | 104900 | 0.3045        |
+| 1.6435 | 105000 | 0.2851        |
+| 1.6451 | 105100 | 0.2824        |
+| 1.6466 | 105200 | 0.3015        |
+| 1.6482 | 105300 | 0.252         |
+| 1.6497 | 105400 | 0.2719        |
+| 1.6513 | 105500 | 0.2942        |
+| 1.6529 | 105600 | 0.2768        |
+| 1.6544 | 105700 | 0.2724        |
+| 1.6560 | 105800 | 0.2595        |
+| 1.6576 | 105900 | 0.2801        |
+| 1.6591 | 106000 | 0.3121        |
+| 1.6607 | 106100 | 0.2791        |
+| 1.6623 | 106200 | 0.2373        |
+| 1.6638 | 106300 | 0.2842        |
+| 1.6654 | 106400 | 0.2715        |
+| 1.6670 | 106500 | 0.2758        |
+| 1.6685 | 106600 | 0.2677        |
+| 1.6701 | 106700 | 0.2673        |
+| 1.6717 | 106800 | 0.2767        |
+| 1.6732 | 106900 | 0.2546        |
+| 1.6748 | 107000 | 0.2773        |
+| 1.6764 | 107100 | 0.2728        |
+| 1.6779 | 107200 | 0.3119        |
+| 1.6795 | 107300 | 0.2454        |
+| 1.6811 | 107400 | 0.2313        |
+| 1.6826 | 107500 | 0.2352        |
+| 1.6842 | 107600 | 0.2234        |
+| 1.6857 | 107700 | 0.239         |
+| 1.6873 | 107800 | 0.2529        |
+| 1.6889 | 107900 | 0.2874        |
+| 1.6904 | 108000 | 0.2261        |
+| 1.6920 | 108100 | 0.2577        |
+| 1.6936 | 108200 | 0.1774        |
+| 1.6951 | 108300 | 0.2084        |
+| 1.6967 | 108400 | 0.2629        |
+| 1.6983 | 108500 | 0.2257        |
+| 1.6998 | 108600 | 0.2365        |
+| 1.7014 | 108700 | 0.2344        |
+| 1.7030 | 108800 | 0.2513        |
+| 1.7045 | 108900 | 0.2278        |
+| 1.7061 | 109000 | 0.2437        |
+| 1.7077 | 109100 | 0.2383        |
+| 1.7092 | 109200 | 0.2668        |
+| 1.7108 | 109300 | 0.2273        |
+| 1.7124 | 109400 | 0.2086        |
+| 1.7139 | 109500 | 0.1963        |
+| 1.7155 | 109600 | 0.2364        |
+| 1.7171 | 109700 | 0.2005        |
+| 1.7186 | 109800 | 0.2093        |
+| 1.7202 | 109900 | 0.2159        |
+| 1.7217 | 110000 | 0.2148        |
+| 1.7233 | 110100 | 0.2278        |
+| 1.7249 | 110200 | 0.2088        |
+| 1.7264 | 110300 | 0.2089        |
+| 1.7280 | 110400 | 0.1923        |
+| 1.7296 | 110500 | 0.2446        |
+| 1.7311 | 110600 | 0.2016        |
+| 1.7327 | 110700 | 0.184         |
+| 1.7343 | 110800 | 0.1578        |
+| 1.7358 | 110900 | 0.2128        |
+| 1.7374 | 111000 | 0.2003        |
+| 1.7390 | 111100 | 0.182         |
+| 1.7405 | 111200 | 0.1611        |
+| 1.7421 | 111300 | 0.1827        |
+| 1.7437 | 111400 | 0.1856        |
+| 1.7452 | 111500 | 0.1907        |
+| 1.7468 | 111600 | 0.1784        |
+| 1.7484 | 111700 | 0.1955        |
+| 1.7499 | 111800 | 0.1594        |
+| 1.7515 | 111900 | 0.1786        |
+| 1.7531 | 112000 | 0.172         |
+| 1.7546 | 112100 | 0.1593        |
+| 1.7562 | 112200 | 0.1878        |
+| 1.7577 | 112300 | 0.1819        |
+| 1.7593 | 112400 | 0.1674        |
+| 1.7609 | 112500 | 0.1647        |
+| 1.7624 | 112600 | 0.1513        |
+| 1.7640 | 112700 | 0.1756        |
+| 1.7656 | 112800 | 0.1676        |
+| 1.7671 | 112900 | 0.2208        |
+| 1.7687 | 113000 | 0.1695        |
+| 1.7703 | 113100 | 0.171         |
+| 1.7718 | 113200 | 0.1504        |
+| 1.7734 | 113300 | 0.1963        |
+| 1.7750 | 113400 | 0.1613        |
+| 1.7765 | 113500 | 0.1516        |
+| 1.7781 | 113600 | 0.171         |
+| 1.7797 | 113700 | 0.1855        |
+| 1.7812 | 113800 | 0.1556        |
+| 1.7828 | 113900 | 0.1695        |
+| 1.7844 | 114000 | 0.1521        |
+| 1.7859 | 114100 | 0.1541        |
+| 1.7875 | 114200 | 0.186         |
+| 1.7891 | 114300 | 0.1724        |
+| 1.7906 | 114400 | 0.1767        |
+| 1.7922 | 114500 | 0.157         |
+| 1.7937 | 114600 | 0.1377        |
+| 1.7953 | 114700 | 0.155         |
+| 1.7969 | 114800 | 0.1802        |
+| 1.7984 | 114900 | 0.1735        |
+| 1.8000 | 115000 | 0.1253        |
+| 1.8016 | 115100 | 0.1366        |
+| 1.8031 | 115200 | 0.1524        |
+| 1.8047 | 115300 | 0.1391        |
+| 1.8063 | 115400 | 0.1282        |
+| 1.8078 | 115500 | 0.1506        |
+| 1.8094 | 115600 | 0.1474        |
+| 1.8110 | 115700 | 0.1603        |
+| 1.8125 | 115800 | 0.1619        |
+| 1.8141 | 115900 | 0.1548        |
+| 1.8157 | 116000 | 0.1446        |
+| 1.8172 | 116100 | 0.1555        |
+| 1.8188 | 116200 | 0.1374        |
+| 1.8204 | 116300 | 0.1294        |
+| 1.8219 | 116400 | 0.1445        |
+| 1.8235 | 116500 | 0.1305        |
+| 1.8251 | 116600 | 0.1353        |
+| 1.8266 | 116700 | 0.1207        |
+| 1.8282 | 116800 | 0.1293        |
+| 1.8297 | 116900 | 0.1313        |
+| 1.8313 | 117000 | 0.1413        |
+| 1.8329 | 117100 | 0.1537        |
+| 1.8344 | 117200 | 0.133         |
+| 1.8360 | 117300 | 0.1624        |
+| 1.8376 | 117400 | 0.1486        |
+| 1.8391 | 117500 | 0.1353        |
+| 1.8407 | 117600 | 0.1174        |
+| 1.8423 | 117700 | 0.1509        |
+| 1.8438 | 117800 | 0.1295        |
+| 1.8454 | 117900 | 0.1341        |
+| 1.8470 | 118000 | 0.1205        |
+| 1.8485 | 118100 | 0.1114        |
+| 1.8501 | 118200 | 0.1387        |
+| 1.8517 | 118300 | 0.1346        |
+| 1.8532 | 118400 | 0.1551        |
+| 1.8548 | 118500 | 0.1106        |
+| 1.8564 | 118600 | 0.1521        |
+| 1.8579 | 118700 | 0.1048        |
+| 1.8595 | 118800 | 0.1694        |
+| 1.8611 | 118900 | 0.1297        |
+| 1.8626 | 119000 | 0.1619        |
+| 1.8642 | 119100 | 0.1221        |
+| 1.8657 | 119200 | 0.1151        |
+| 1.8673 | 119300 | 0.1459        |
+| 1.8689 | 119400 | 0.1153        |
+| 1.8704 | 119500 | 0.1329        |
+| 1.8720 | 119600 | 0.134         |
+| 1.8736 | 119700 | 0.1243        |
+| 1.8751 | 119800 | 0.1229        |
+| 1.8767 | 119900 | 0.1184        |
+| 1.8783 | 120000 | 0.1001        |
+| 1.8798 | 120100 | 0.1314        |
+| 1.8814 | 120200 | 0.1307        |
+| 1.8830 | 120300 | 0.1134        |
+| 1.8845 | 120400 | 0.1241        |
+| 1.8861 | 120500 | 0.114         |
+| 1.8877 | 120600 | 0.124         |
+| 1.8892 | 120700 | 0.1056        |
+| 1.8908 | 120800 | 0.1154        |
+| 1.8924 | 120900 | 0.1056        |
+| 1.8939 | 121000 | 0.1245        |
+| 1.8955 | 121100 | 0.129         |
+| 1.8971 | 121200 | 0.111         |
+| 1.8986 | 121300 | 0.1347        |
+| 1.9002 | 121400 | 0.1087        |
+| 1.9017 | 121500 | 0.1078        |
+| 1.9033 | 121600 | 0.1047        |
+| 1.9049 | 121700 | 0.1347        |
+| 1.9064 | 121800 | 0.114         |
+| 1.9080 | 121900 | 0.1208        |
+| 1.9096 | 122000 | 0.081         |
+| 1.9111 | 122100 | 0.0903        |
+| 1.9127 | 122200 | 0.1054        |
+| 1.9143 | 122300 | 0.0991        |
+| 1.9158 | 122400 | 0.1142        |
+| 1.9174 | 122500 | 0.1154        |
+| 1.9190 | 122600 | 0.0897        |
+| 1.9205 | 122700 | 0.1036        |
+| 1.9221 | 122800 | 0.1321        |
+| 1.9237 | 122900 | 0.1037        |
+| 1.9252 | 123000 | 0.069         |
+| 1.9268 | 123100 | 0.0959        |
+| 1.9284 | 123200 | 0.0957        |
+| 1.9299 | 123300 | 0.1062        |
+| 1.9315 | 123400 | 0.0963        |
+| 1.9331 | 123500 | 0.0949        |
+| 1.9346 | 123600 | 0.0897        |
+| 1.9362 | 123700 | 0.102         |
+| 1.9377 | 123800 | 0.0937        |
+| 1.9393 | 123900 | 0.095         |
+| 1.9409 | 124000 | 0.1067        |
+| 1.9424 | 124100 | 0.0731        |
+| 1.9440 | 124200 | 0.1025        |
+| 1.9456 | 124300 | 0.113         |
+| 1.9471 | 124400 | 0.0887        |
+| 1.9487 | 124500 | 0.0938        |
+| 1.9503 | 124600 | 0.0863        |
+| 1.9518 | 124700 | 0.1005        |
+| 1.9534 | 124800 | 0.1084        |
+| 1.9550 | 124900 | 0.0923        |
+| 1.9565 | 125000 | 0.086         |
+| 1.9581 | 125100 | 0.0899        |
+| 1.9597 | 125200 | 0.1179        |
+| 1.9612 | 125300 | 0.0989        |
+| 1.9628 | 125400 | 0.1225        |
+| 1.9644 | 125500 | 0.1126        |
+| 1.9659 | 125600 | 0.092         |
+| 1.9675 | 125700 | 0.0953        |
+| 1.9691 | 125800 | 0.1162        |
+| 1.9706 | 125900 | 0.113         |
+| 1.9722 | 126000 | 0.07          |
+| 1.9737 | 126100 | 0.0654        |
+| 1.9753 | 126200 | 0.0735        |
+| 1.9769 | 126300 | 0.0937        |
+| 1.9784 | 126400 | 0.1095        |
+| 1.9800 | 126500 | 0.0677        |
+| 1.9816 | 126600 | 0.0928        |
+| 1.9831 | 126700 | 0.0847        |
+| 1.9847 | 126800 | 0.0871        |
+| 1.9863 | 126900 | 0.0748        |
+| 1.9878 | 127000 | 0.0577        |
+| 1.9894 | 127100 | 0.0674        |
+| 1.9910 | 127200 | 0.059         |
+| 1.9925 | 127300 | 0.1051        |
+| 1.9941 | 127400 | 0.0723        |
+| 1.9957 | 127500 | 0.076         |
+| 1.9972 | 127600 | 0.123         |
+| 1.9988 | 127700 | 0.166         |
+| 2.0004 | 127800 | 0.1987        |
+| 2.0019 | 127900 | 0.0239        |
+| 2.0035 | 128000 | 0.0281        |
+| 2.0051 | 128100 | 0.0204        |
+| 2.0066 | 128200 | 0.0287        |
+| 2.0082 | 128300 | 0.0507        |
+| 2.0098 | 128400 | 0.0425        |
+| 2.0113 | 128500 | 0.2174        |
+| 2.0129 | 128600 | 0.4736        |
+| 2.0145 | 128700 | 0.7072        |
+| 2.0160 | 128800 | 0.4264        |
+| 2.0176 | 128900 | 0.3925        |
+| 2.0192 | 129000 | 0.4464        |
+| 2.0207 | 129100 | 0.4491        |
+| 2.0223 | 129200 | 0.4134        |
+| 2.0239 | 129300 | 0.3076        |
+| 2.0254 | 129400 | 0.3543        |
+| 2.0270 | 129500 | 0.39          |
+| 2.0285 | 129600 | 0.4264        |
+| 2.0301 | 129700 | 0.5531        |
+| 2.0317 | 129800 | 0.3795        |
+| 2.0332 | 129900 | 0.3731        |
+| 2.0348 | 130000 | 0.3682        |
+| 2.0364 | 130100 | 0.3475        |
+| 2.0379 | 130200 | 0.3145        |
+| 2.0395 | 130300 | 0.3439        |
+| 2.0411 | 130400 | 0.2909        |
+| 2.0426 | 130500 | 0.3694        |
+| 2.0442 | 130600 | 0.3264        |
+| 2.0458 | 130700 | 0.3285        |
+| 2.0473 | 130800 | 0.291         |
+| 2.0489 | 130900 | 0.2715        |
+| 2.0505 | 131000 | 0.3234        |
+| 2.0520 | 131100 | 0.333         |
+| 2.0536 | 131200 | 0.3547        |
+| 2.0552 | 131300 | 0.3735        |
+| 2.0567 | 131400 | 0.3693        |
+| 2.0583 | 131500 | 0.373         |
+| 2.0599 | 131600 | 0.3451        |
+| 2.0614 | 131700 | 0.3508        |
+| 2.0630 | 131800 | 0.3627        |
+| 2.0645 | 131900 | 0.3881        |
+| 2.0661 | 132000 | 0.3705        |
+| 2.0677 | 132100 | 0.3743        |
+| 2.0692 | 132200 | 0.3963        |
+| 2.0708 | 132300 | 0.3693        |
+| 2.0724 | 132400 | 0.3855        |
+| 2.0739 | 132500 | 0.3695        |
+| 2.0755 | 132600 | 0.3863        |
+| 2.0771 | 132700 | 0.373         |
+| 2.0786 | 132800 | 0.4406        |
+| 2.0802 | 132900 | 0.3888        |
+| 2.0818 | 133000 | 0.4662        |
+| 2.0833 | 133100 | 0.3748        |
+| 2.0849 | 133200 | 0.396         |
+| 2.0865 | 133300 | 0.3977        |
+| 2.0880 | 133400 | 0.4074        |
+| 2.0896 | 133500 | 0.3608        |
+| 2.0912 | 133600 | 0.3524        |
+| 2.0927 | 133700 | 0.3304        |
+| 2.0943 | 133800 | 0.3207        |
+| 2.0959 | 133900 | 0.377         |
+| 2.0974 | 134000 | 0.3051        |
+| 2.0990 | 134100 | 0.3258        |
+| 2.1005 | 134200 | 0.3023        |
+| 2.1021 | 134300 | 0.3184        |
+| 2.1037 | 134400 | 0.3028        |
+| 2.1052 | 134500 | 0.3825        |
+| 2.1068 | 134600 | 0.3204        |
+| 2.1084 | 134700 | 0.344         |
+| 2.1099 | 134800 | 0.318         |
+| 2.1115 | 134900 | 0.3249        |
+| 2.1131 | 135000 | 0.3269        |
+| 2.1146 | 135100 | 0.2974        |
+| 2.1162 | 135200 | 0.3061        |
+| 2.1178 | 135300 | 0.319         |
+| 2.1193 | 135400 | 0.333         |
+| 2.1209 | 135500 | 0.3016        |
+| 2.1225 | 135600 | 0.2981        |
+| 2.1240 | 135700 | 0.2871        |
+| 2.1256 | 135800 | 0.3159        |
+| 2.1272 | 135900 | 0.3097        |
+| 2.1287 | 136000 | 0.2933        |
+| 2.1303 | 136100 | 0.2838        |
+| 2.1319 | 136200 | 0.2561        |
+| 2.1334 | 136300 | 0.283         |
+| 2.1350 | 136400 | 0.2988        |
+| 2.1365 | 136500 | 0.3087        |
+| 2.1381 | 136600 | 0.2954        |
+| 2.1397 | 136700 | 0.2699        |
+| 2.1412 | 136800 | 0.3057        |
+| 2.1428 | 136900 | 0.2838        |
+| 2.1444 | 137000 | 0.2774        |
+| 2.1459 | 137100 | 0.2856        |
+| 2.1475 | 137200 | 0.271         |
+| 2.1491 | 137300 | 0.327         |
+| 2.1506 | 137400 | 0.28          |
+| 2.1522 | 137500 | 0.2534        |
+| 2.1538 | 137600 | 0.2553        |
+| 2.1553 | 137700 | 0.2613        |
+| 2.1569 | 137800 | 0.2749        |
+| 2.1585 | 137900 | 0.2289        |
+| 2.1600 | 138000 | 0.3811        |
+| 2.1616 | 138100 | 0.283         |
+| 2.1632 | 138200 | 0.2693        |
+| 2.1647 | 138300 | 0.2463        |
+| 2.1663 | 138400 | 0.2403        |
+| 2.1679 | 138500 | 0.2759        |
+| 2.1694 | 138600 | 0.238         |
+| 2.1710 | 138700 | 0.2633        |
+| 2.1725 | 138800 | 0.2136        |
+| 2.1741 | 138900 | 0.2511        |
+| 2.1757 | 139000 | 0.2302        |
+| 2.1772 | 139100 | 0.2359        |
+| 2.1788 | 139200 | 0.2268        |
+| 2.1804 | 139300 | 0.2805        |
+| 2.1819 | 139400 | 0.2489        |
+| 2.1835 | 139500 | 0.1915        |
+| 2.1851 | 139600 | 0.2726        |
+| 2.1866 | 139700 | 0.2383        |
+| 2.1882 | 139800 | 0.2572        |
+| 2.1898 | 139900 | 0.2453        |
+| 2.1913 | 140000 | 0.2388        |
+| 2.1929 | 140100 | 0.238         |
+| 2.1945 | 140200 | 0.2578        |
+| 2.1960 | 140300 | 0.2592        |
+| 2.1976 | 140400 | 0.2866        |
+| 2.1992 | 140500 | 0.2512        |
+| 2.2007 | 140600 | 0.2368        |
+| 2.2023 | 140700 | 0.25          |
+| 2.2039 | 140800 | 0.2809        |
+| 2.2054 | 140900 | 0.2504        |
+| 2.2070 | 141000 | 0.2436        |
+| 2.2085 | 141100 | 0.2227        |
+| 2.2101 | 141200 | 0.2179        |
+| 2.2117 | 141300 | 0.2724        |
+| 2.2132 | 141400 | 0.2844        |
+| 2.2148 | 141500 | 0.206         |
+| 2.2164 | 141600 | 0.2177        |
+| 2.2179 | 141700 | 0.2809        |
+| 2.2195 | 141800 | 0.2447        |
+| 2.2211 | 141900 | 0.2409        |
+| 2.2226 | 142000 | 0.2327        |
+| 2.2242 | 142100 | 0.2077        |
+| 2.2258 | 142200 | 0.2768        |
+| 2.2273 | 142300 | 0.2383        |
+| 2.2289 | 142400 | 0.2939        |
+| 2.2305 | 142500 | 0.26          |
+| 2.2320 | 142600 | 0.251         |
+| 2.2336 | 142700 | 0.2318        |
+| 2.2352 | 142800 | 0.1949        |
+| 2.2367 | 142900 | 0.2186        |
+| 2.2383 | 143000 | 0.2659        |
+| 2.2399 | 143100 | 0.2436        |
+| 2.2414 | 143200 | 0.247         |
+| 2.2430 | 143300 | 0.2757        |
+| 2.2445 | 143400 | 0.288         |
+| 2.2461 | 143500 | 0.2453        |
+| 2.2477 | 143600 | 0.2856        |
+| 2.2492 | 143700 | 0.2832        |
+| 2.2508 | 143800 | 0.2654        |
+| 2.2524 | 143900 | 0.2647        |
+| 2.2539 | 144000 | 0.3071        |
+| 2.2555 | 144100 | 0.2667        |
+| 2.2571 | 144200 | 0.2684        |
+| 2.2586 | 144300 | 0.2612        |
+| 2.2602 | 144400 | 0.2608        |
+| 2.2618 | 144500 | 0.2471        |
+| 2.2633 | 144600 | 0.2814        |
+| 2.2649 | 144700 | 0.2707        |
+| 2.2665 | 144800 | 0.2828        |
+| 2.2680 | 144900 | 0.2145        |
+| 2.2696 | 145000 | 0.271         |
+| 2.2712 | 145100 | 0.2851        |
+| 2.2727 | 145200 | 0.248         |
+| 2.2743 | 145300 | 0.3098        |
+| 2.2759 | 145400 | 0.2695        |
+| 2.2774 | 145500 | 0.2668        |
+| 2.2790 | 145600 | 0.2572        |
+| 2.2805 | 145700 | 0.2885        |
+| 2.2821 | 145800 | 0.2721        |
+| 2.2837 | 145900 | 0.257         |
+| 2.2852 | 146000 | 0.2546        |
+| 2.2868 | 146100 | 0.2441        |
+| 2.2884 | 146200 | 0.2809        |
+| 2.2899 | 146300 | 0.245         |
+| 2.2915 | 146400 | 0.2691        |
+| 2.2931 | 146500 | 0.3119        |
+| 2.2946 | 146600 | 0.2677        |
+| 2.2962 | 146700 | 0.2964        |
+| 2.2978 | 146800 | 0.262         |
+| 2.2993 | 146900 | 0.3017        |
+| 2.3009 | 147000 | 0.2972        |
+| 2.3025 | 147100 | 0.2875        |
+| 2.3040 | 147200 | 0.278         |
+| 2.3056 | 147300 | 0.238         |
+| 2.3072 | 147400 | 0.2174        |
+| 2.3087 | 147500 | 0.2652        |
+| 2.3103 | 147600 | 0.2951        |
+| 2.3119 | 147700 | 0.2618        |
+| 2.3134 | 147800 | 0.2474        |
+| 2.3150 | 147900 | 0.2408        |
+| 2.3165 | 148000 | 0.269         |
+| 2.3181 | 148100 | 0.2263        |
+| 2.3197 | 148200 | 0.2499        |
+| 2.3212 | 148300 | 0.2954        |
+| 2.3228 | 148400 | 0.2497        |
+| 2.3244 | 148500 | 0.2684        |
+| 2.3259 | 148600 | 0.2086        |
+| 2.3275 | 148700 | 0.2425        |
+| 2.3291 | 148800 | 0.2498        |
+| 2.3306 | 148900 | 0.2225        |
+| 2.3322 | 149000 | 0.2547        |
+| 2.3338 | 149100 | 0.2188        |
+| 2.3353 | 149200 | 0.2664        |
+| 2.3369 | 149300 | 0.2607        |
+| 2.3385 | 149400 | 0.2084        |
+| 2.3400 | 149500 | 0.2328        |
+| 2.3416 | 149600 | 0.2096        |
+| 2.3432 | 149700 | 0.2531        |
+| 2.3447 | 149800 | 0.2256        |
+| 2.3463 | 149900 | 0.2123        |
+| 2.3479 | 150000 | 0.2668        |
+| 2.3494 | 150100 | 0.2562        |
+| 2.3510 | 150200 | 0.2527        |
+| 2.3525 | 150300 | 0.2416        |
+| 2.3541 | 150400 | 0.2732        |
+| 2.3557 | 150500 | 0.2435        |
+| 2.3572 | 150600 | 0.2446        |
+| 2.3588 | 150700 | 0.2728        |
+| 2.3604 | 150800 | 0.2603        |
+| 2.3619 | 150900 | 0.3144        |
+| 2.3635 | 151000 | 0.2644        |
+| 2.3651 | 151100 | 0.2676        |
+| 2.3666 | 151200 | 0.3062        |
+| 2.3682 | 151300 | 0.2505        |
+| 2.3698 | 151400 | 0.2715        |
+| 2.3713 | 151500 | 0.2733        |
+| 2.3729 | 151600 | 0.3129        |
+| 2.3745 | 151700 | 0.291         |
+| 2.3760 | 151800 | 0.2842        |
+| 2.3776 | 151900 | 0.3183        |
+| 2.3792 | 152000 | 0.2372        |
+| 2.3807 | 152100 | 0.2588        |
+| 2.3823 | 152200 | 0.1666        |
+| 2.3839 | 152300 | 0.1011        |
+| 2.3854 | 152400 | 0.1493        |
+| 2.3870 | 152500 | 0.1348        |
+| 2.3885 | 152600 | 0.1179        |
+| 2.3901 | 152700 | 0.1373        |
+| 2.3917 | 152800 | 0.1212        |
+| 2.3932 | 152900 | 0.1135        |
+| 2.3948 | 153000 | 0.1335        |
+| 2.3964 | 153100 | 0.1458        |
+| 2.3979 | 153200 | 0.1259        |
+| 2.3995 | 153300 | 0.1459        |
+| 2.4011 | 153400 | 0.1232        |
+| 2.4026 | 153500 | 0.1172        |
+| 2.4042 | 153600 | 0.0911        |
+| 2.4058 | 153700 | 0.1177        |
+| 2.4073 | 153800 | 0.0954        |
+| 2.4089 | 153900 | 0.107         |
+| 2.4105 | 154000 | 0.1355        |
+| 2.4120 | 154100 | 0.1012        |
+| 2.4136 | 154200 | 0.092         |
+| 2.4152 | 154300 | 0.0958        |
+| 2.4167 | 154400 | 0.1014        |
+| 2.4183 | 154500 | 0.1202        |
+| 2.4199 | 154600 | 0.0954        |
+| 2.4214 | 154700 | 0.097         |
+| 2.4230 | 154800 | 0.1103        |
+| 2.4245 | 154900 | 0.1274        |
+| 2.4261 | 155000 | 0.1015        |
+| 2.4277 | 155100 | 0.1051        |
+| 2.4292 | 155200 | 0.1225        |
+| 2.4308 | 155300 | 0.1555        |
+| 2.4324 | 155400 | 0.0811        |
+| 2.4339 | 155500 | 0.0947        |
+| 2.4355 | 155600 | 0.1104        |
+| 2.4371 | 155700 | 0.0911        |
+| 2.4386 | 155800 | 0.0705        |
+| 2.4402 | 155900 | 0.0776        |
+| 2.4418 | 156000 | 0.0984        |
+| 2.4433 | 156100 | 0.0797        |
+| 2.4449 | 156200 | 0.1321        |
+| 2.4465 | 156300 | 0.075         |
+| 2.4480 | 156400 | 0.072         |
+| 2.4496 | 156500 | 0.0887        |
+| 2.4512 | 156600 | 0.1088        |
+| 2.4527 | 156700 | 0.0838        |
+| 2.4543 | 156800 | 0.109         |
+| 2.4559 | 156900 | 0.0821        |
+| 2.4574 | 157000 | 0.1076        |
+| 2.4590 | 157100 | 0.0959        |
+| 2.4605 | 157200 | 0.1065        |
+| 2.4621 | 157300 | 0.1038        |
+| 2.4637 | 157400 | 0.0978        |
+| 2.4652 | 157500 | 0.0831        |
+| 2.4668 | 157600 | 0.1033        |
+| 2.4684 | 157700 | 0.1046        |
+| 2.4699 | 157800 | 0.1136        |
+| 2.4715 | 157900 | 0.0833        |
+| 2.4731 | 158000 | 0.0796        |
+| 2.4746 | 158100 | 0.0836        |
+| 2.4762 | 158200 | 0.1213        |
+| 2.4778 | 158300 | 0.0865        |
+| 2.4793 | 158400 | 0.0767        |
+| 2.4809 | 158500 | 0.074         |
+| 2.4825 | 158600 | 0.0826        |
+| 2.4840 | 158700 | 0.0758        |
+| 2.4856 | 158800 | 0.0767        |
+| 2.4872 | 158900 | 0.1284        |
+| 2.4887 | 159000 | 0.0826        |
+| 2.4903 | 159100 | 0.0921        |
+| 2.4919 | 159200 | 0.0746        |
+| 2.4934 | 159300 | 0.0865        |
+| 2.4950 | 159400 | 0.0771        |
+| 2.4965 | 159500 | 0.0844        |
+| 2.4981 | 159600 | 0.0682        |
+| 2.4997 | 159700 | 0.068         |
+| 2.5012 | 159800 | 0.0674        |
+| 2.5028 | 159900 | 0.0942        |
+| 2.5044 | 160000 | 0.066         |
+
+</details>
+
+### Framework Versions
+- Python: 3.11.12
+- Sentence Transformers: 5.1.2
+- Transformers: 4.51.3
+- PyTorch: 2.8.0+cu128
+- Accelerate: 1.5.2
+- Datasets: 2.21.0
+- Tokenizers: 0.21.4
 
 ## Citation
+
+### BibTeX
+
+#### Sentence Transformers
+```bibtex
+@inproceedings{reimers-2019-sentence-bert,
+    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
+    author = "Reimers, Nils and Gurevych, Iryna",
+    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
+    month = "11",
+    year = "2019",
+    publisher = "Association for Computational Linguistics",
+    url = "https://arxiv.org/abs/1908.10084",
+}
+```
+
+#### SpladeLoss
+```bibtex
+@misc{formal2022distillationhardnegativesampling,
+      title={From Distillation to Hard Negative Sampling: Making Sparse Neural IR Models More Effective},
+      author={Thibault Formal and Carlos Lassance and Benjamin Piwowarski and Stéphane Clinchant},
+      year={2022},
+      eprint={2205.04733},
+      archivePrefix={arXiv},
+      primaryClass={cs.IR},
+      url={https://arxiv.org/abs/2205.04733},
+}
+```
+
+#### SparseMultipleNegativesRankingLoss
+```bibtex
+@misc{henderson2017efficient,
+    title={Efficient Natural Language Response Suggestion for Smart Reply},
+    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},
+    year={2017},
+    eprint={1705.00652},
+    archivePrefix={arXiv},
+    primaryClass={cs.CL}
+}
 ```
-@misc{TelePIX-PIXIE-Splade-v1.0,
-  title={PIXIE-Splade-v1.0},
-  author={TelePIX AI Research Team and Bongmin Kim},
-  year={2026},
-  url={https://huggingface.co/telepix/PIXIE-Splade-v1.0}
+
+#### FlopsLoss
+```bibtex
+@article{paria2020minimizing,
+    title={Minimizing flops to learn efficient sparse representations},
+    author={Paria, Biswajit and Yeh, Chih-Kuan and Yen, Ian EH and Xu, Ning and Ravikumar, Pradeep and P{'o}czos, Barnab{'a}s},
+    journal={arXiv preprint arXiv:2004.05665},
+    year={2020}
 }
 ```
 
-## Contact
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
 
-If you have any suggestions or questions about the PIXIE, please reach out to the authors at bmkim@telepix.net.
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+--> 

added_tokens.json

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+}

config.json

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+}

config_sentence_transformers.json

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+{
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+  "__version__": {
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+    "pytorch": "2.8.0+cu128"
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+}

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+size 597503064

modules.json

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+    "name": "0",
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+    "type": "sentence_transformers.sparse_encoder.models.MLMTransformer"
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+    "type": "sentence_transformers.sparse_encoder.models.SpladePooling"
+  }
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sentence_bert_config.json

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+    "do_lower_case": false
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