---
base_model: lemon-mint/mMiniLMv2-L12-H384-Distilled-Iter12-final
datasets: []
language: []
library_name: sentence-transformers
metrics:
- pearson_cosine
- spearman_cosine
- pearson_manhattan
- spearman_manhattan
- pearson_euclidean
- spearman_euclidean
- pearson_dot
- spearman_dot
- pearson_max
- spearman_max
- cosine_accuracy@1
- cosine_accuracy@3
- cosine_accuracy@5
- cosine_accuracy@10
- cosine_precision@1
- cosine_precision@3
- cosine_precision@5
- cosine_precision@10
- cosine_recall@1
- cosine_recall@3
- cosine_recall@5
- cosine_recall@10
- cosine_ndcg@10
- cosine_mrr@10
- cosine_map@100
- dot_accuracy@1
- dot_accuracy@3
- dot_accuracy@5
- dot_accuracy@10
- dot_precision@1
- dot_precision@3
- dot_precision@5
- dot_precision@10
- dot_recall@1
- dot_recall@3
- dot_recall@5
- dot_recall@10
- dot_ndcg@10
- dot_mrr@10
- dot_map@100
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:480616
- loss:MSELoss
widget:
- source_sentence: 'passage: Here is how to compress a file in Terminal:
1. Use the `ls` command to list the files in the current directory. Confirm the
presence of the file you wish to compress and note the precise spelling and case
sensitivity of the file.
2. To zip a single file called "example_file.txt," enter the following command:
```csharp
zip my_compressed_archive.zip example_file.txt
```
Replace "my_compressed_archive" with your own unique identifier and adjust "example_file.txt"
according to your actual file name. Press Enter to execute the command.'
sentences:
- 'passage: Regular moisturizing is crucial to prevent and alleviate dry skin under
your nose. Opt for non-comedogenic moisturizers, which are less likely to clog
pores, and apply them immediately after washing your face while your skin is still
damp to lock in moisture.
Non-comedogenic means the product doesn''t contain ingredients known to block
pores. Heavier creams might be necessary in winter months when indoor heating
systems sap moisture from the air.'
- 'query: 유럽 몰도바 국민 대회의 목적은 무엇이었습니까?'
- 'passage: When planning dates with your significant other, consider including
activities that both adults and children can enjoy together. Examples include
trips to the zoo, bowling alleys, miniature golf courses, or movie nights at home
with kid-friendly films. Including your children in these outings allows them
to spend quality time with you while also giving you and your partner opportunities
to connect.
It''s crucial to establish clear boundaries and expectations surrounding dating
and relationships within your household. Discuss topics like privacy, personal
space, and appropriate behavior with your children. Explain that although you
may have feelings for your partner, maintaining healthy familial bonds remains
paramount.'
- source_sentence: "passage: 엄마는 치어리딩의 안전에 대해 우려할 수 있으므로 안전 조치에 대해 확신을 심어줘야 합니다. 치어리딩\
\ 트라이아웃과 연습 중 안전 프로토콜, 장비, 코치 자격, 비상 절차 등을 자세히 알려드리세요. \n\n치어리딩 안전에 대한 추가 과정이나\
\ 워크숍에 참여하여 엄마의 신뢰를 더욱 높일 수 있습니다. 다른 스포츠와 비교하여 치어리딩 부상 통계를 제공하고, 연습과 공연 중에 사용되는\
\ 보호 장비를 설명하고, 치어리딩 안전 모범 사례에 중점을 둔 자료를 소개하는 것도 도움이 될 수 있습니다."
sentences:
- 'passage: 스페이스바 문제는 키보드 드라이버가 오래되거나 손상되었을 수 있습니다. 키보드 드라이버를 업데이트하는 방법은 다음과 같습니다.
1. `Win + X`를 동시에 누르고 장치 관리자를 선택합니다.
2. ''키보드''라는 범주를 찾아 확장합니다. 키보드 모델을 마우스 오른쪽 버튼으로 클릭하고 ''드라이버 업데이트''를 선택합니다.
3. ''업데이트된 드라이버 소프트웨어를 자동으로 검색''을 선택하고 Windows에서 사용 가능한 업데이트를 찾도록 합니다. 업데이트가 표시되면
설치합니다.
4. PC를 다시 시작하고 나중에 스페이스바를 테스트합니다.
드라이버를 업데이트해도 문제가 해결되지 않으면 다른 문제 해결사를 실행해 볼 수 있습니다. Windows에는 일반적인 시스템 오류를 진단하고
해결하도록 설계된 기본 제공 도구가 있습니다. 하드웨어 및 장치 문제 해결사를 실행하려면 다음 단계를 따르십시오.
1. `Win + I`를 눌러 설정을 엽니다. ''시스템 > 문제 해결 > 기타 문제 해결사''로 이동합니다.
2. ''하드웨어 및 장치''라는 옵션이 나타날 때까지 아래로 스크롤합니다. 옆에 있는 ''실행''을 클릭합니다.
3. 문제 해결사가 잠재적인 문제를 검색하기 시작합니다. 프로세스가 완료될 때까지 기다립니다. 제안된 해결책이 있으면 해결하십시오.
4. 문제 해결사를 실행한 후 PC를 다시 시작하고 스페이스바가 제대로 작동하는지 확인하십시오.'
- 'query: 1880년 남성 테니스 시즌에서 가장 많이 우승한 선수는 누구입니까?'
- 'passage: 가족 구성원을 내쫓는 것은 모든 당사자에게 심각한 감정적 결과를 초래할 수 있는 극단적인 조치이므로 신중하게 고려해야 합니다.
이 가이드는 내쫓는 것이 무엇을 의미하는지 그리고 상황에 맞는 선택인지 이해하는 데 도움이 되는 포괄적인 단계를 제공합니다. 이 기사는 가족
구성원을 내쫓는 것을 옹호하거나 권장하지 않으며 사용자의 요청에 따라 정보를 제공합니다.
내쫓는 것을 고려하는 첫 번째 단계는 가족 구성원을 내쫓고 싶은 이유를 평가하는 것입니다. 일반적인 이유로는 지속적인 학대(신체적, 정신적
또는 재정적), 중독 문제, 심각한 성격 차이 또는 유해한 행동이 있습니다. 치료사, 상담사 또는 신뢰할 수 있는 종교 지도자와 같은 전문가의
조언을 구하여 대안적인 관점과 대처 전략을 제공받을 수 있습니다. 내쫓는 것이 근본적인 문제를 해결하지 못하고 오히려 악화될 수 있다는 점을
기억하십시오.'
- source_sentence: 'passage: Slow roasting a pig shoulder is a delicate process. Set
your oven to 300°F (150°C). Place the prepared pork shoulder in a Dutch oven or
deep baking dish, add liquid (like broth, beer, or cider), cover tightly, and
cook low and slow for approximately 6 hours or until internal temperature reaches
195°F (90°C).
Low-temperature roasting breaks down tough collagen fibers transforming them into
gelatin, making the meat succulent. Covering maintains humidity necessary for
tenderization. Adding liquid deglazes pan drippings, integrating additional flavors.
An accurate thermometer probe inserted into the thickest part avoids under/overcooking.'
sentences:
- 'query: 스트릭틀리 컴 댄싱의 탈락 기준은 매 시즌마다 바뀌나요?'
- 'passage: 에키노칵투스는 황금 술통 선인장 (Echinocactus grusonii)과 할머니 선인장 (Echinocactus polycephalus)과
같은 여러 인기 종을 포함하는 선인장 속입니다. 이 선인장들은 독특한 모양, 아름다운 가시와 낮은 관리 요구 사항으로 유명합니다. 그러나 번창하려면
여전히 적절한 관리가 필요합니다. 이 가이드는 에키노칵투스를 관리하는 방법에 대한 자세한 단계를 제공하여 건강과 장수를 보장합니다.
첫째, 적합한 화분과 흙을 선택하는 것이 중요합니다. 과도한 수분으로 인한 뿌리 부패를 방지하기 위해 배수구가 있는 테라코타 또는 무광택 도자기
화분을 선택하십시오. 화분은 선인장의 현재 크기보다 약간만 크게 해야 합니다. 너무 큰 화분은 물이 고인 흙으로 이어질 수 있습니다.
흙은 배수가 잘 되는 선인장 혼합물을 선택하거나 펄라이트, 거친 모래, 피트모스 또는 퇴비를 같은 비율로 섞어서 직접 만들 수 있습니다. 이
혼합물은 식물의 뿌리에 적절한 통기와 배수를 제공하면서도 약간의 수분을 유지합니다.'
- 'query: α1-아드레날린 수용체는 어떤 약물의 주요 표적입니까?'
- source_sentence: 'query: What is the significance of the New Year''s Smash?'
sentences:
- 'query: What challenges did they face during their travels?'
- "passage: The first step in dressing like Emma Ross is to build a wardrobe filled\
\ with basic items that are versatile and easy to mix and match. \n\nThese basic\
\ items include: skinny, boyfriend, or distressed jeans; plain, striped, or graphic\
\ t-shirts; checkered, flannel, or solid colored blouses; cardigans, pullovers,\
\ or zip-up sweaters; denim, plaid, or A-line skirts; leather, denim, or bomber\
\ jackets; casual sundresses, maxi dresses, or little black dresses; boots, sneakers,\
\ loafers, or ballet flats. \n\nIt's best to choose neutral colors like white,\
\ black, gray, navy blue, and beige for your basics. This will make it easier\
\ to pair different pieces together."
- 'passage: 매치가 성사되면 대화를 시작할 차례입니다. 범블에서는 여성 사용자만 대화를 시작할 수 있으므로 인내심을 갖고 메시지를 기다리세요.
다른 사용자들과 계속 스와이프하고 소통하면서 프로필을 활성화 상태로 유지하는 것도 중요합니다.
여성 사용자가 첫 메시지를 보내면 신속하고 진심으로 답장하세요. 빠른 답장은 열정과 관심을 보여주는 좋은 인상을 줄 수 있습니다. 그녀의 질문에
솔직하게 답하고 그녀의 프로필에 있는 것과 관련된 질문도 해보세요. 의미 있는 대화는 강한 유대감을 형성하고 실제로 만날 가능성을 높입니다.
대화를 시작할 때는 "안녕하세요" 또는 "잘 지내세요"와 같은 일반적인 인사말을 피하세요. 대신 그녀의 프로필에서 특정 요소를 언급하여 진정한
참여를 보여주세요. 또한 정치나 종교와 같이 그녀의 입장을 잘 모르는 상태에서 논란이 될 수 있는 주제는 피하세요.'
- source_sentence: "passage: British Rail produced a variety of railbuses, both as\
\ a means of acquiring new rolling stock cheaply, and to provide economical services\
\ on lightly-used lines. \n\nRailbuses are a very lightweight type of railcar\
\ designed specifically for passenger transport on little-used railway lines.\
\ As the name suggests, they share many aspects of their construction with a bus,\
\ usually having a bus body, or a modified bus body, and having four wheels on\
\ a fixed wheelbase, rather than bogies. Some units were equipped for operation\
\ as diesel multiple units.\n\nIn the late 1950s, British Rail tested a series\
\ of small railbuses, produced by a variety of manufacturers, for about £12,500\
\ each (£261,000 at 2014 prices). These proved to be very economical (on test\
\ the Wickham bus was about ), but were somewhat unreliable. Most of the lines\
\ they worked on were closed following the Beeching Cuts and, being non-standard,\
\ they were all withdrawn in the mid-1960s, so they were never classified under\
\ the TOPS system."
sentences:
- 'query: What aircraft were evaluated under the Advanced Tanker Cargo Aircraft
Program?'
- 'query: What do the results of the 2015 Ogun State House of Assembly election
reveal about the political landscape of Ogun State?'
- 'query: What is the current population of Queenstown?'
model-index:
- name: SentenceTransformer based on lemon-mint/mMiniLMv2-L12-H384-Distilled-Iter12-final
results:
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts dev
type: sts-dev
metrics:
- type: pearson_cosine
value: 0.788594909024035
name: Pearson Cosine
- type: spearman_cosine
value: 0.7955467103677323
name: Spearman Cosine
- type: pearson_manhattan
value: 0.7930435435053418
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.7946895285781183
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.793748458654327
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.7955467103677323
name: Spearman Euclidean
- type: pearson_dot
value: 0.7885949069559369
name: Pearson Dot
- type: spearman_dot
value: 0.7955467103677323
name: Spearman Dot
- type: pearson_max
value: 0.793748458654327
name: Pearson Max
- type: spearman_max
value: 0.7955467103677323
name: Spearman Max
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: Ko StrategyQA dev
type: Ko-StrategyQA-dev
metrics:
- type: cosine_accuracy@1
value: 0.49324324324324326
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.6334459459459459
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.6773648648648649
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.7398648648648649
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.49324324324324326
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.28434684684684686
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.19864864864864865
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.11469594594594595
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.3179214929214929
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.49883365508365507
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.5587717181467181
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.6418798262548262
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.5480892592102351
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.5746521074646076
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.49621317503890106
name: Cosine Map@100
- type: dot_accuracy@1
value: 0.49324324324324326
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.6334459459459459
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.6773648648648649
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.7398648648648649
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.49324324324324326
name: Dot Precision@1
- type: dot_precision@3
value: 0.28434684684684686
name: Dot Precision@3
- type: dot_precision@5
value: 0.19864864864864865
name: Dot Precision@5
- type: dot_precision@10
value: 0.11469594594594595
name: Dot Precision@10
- type: dot_recall@1
value: 0.3179214929214929
name: Dot Recall@1
- type: dot_recall@3
value: 0.49883365508365507
name: Dot Recall@3
- type: dot_recall@5
value: 0.5587717181467181
name: Dot Recall@5
- type: dot_recall@10
value: 0.6418798262548262
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.5480892592102351
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.5746521074646076
name: Dot Mrr@10
- type: dot_map@100
value: 0.49621317503890106
name: Dot Map@100
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts test
type: sts-test
metrics:
- type: pearson_cosine
value: 0.7179974140585698
name: Pearson Cosine
- type: spearman_cosine
value: 0.7184567137414372
name: Spearman Cosine
- type: pearson_manhattan
value: 0.7285069661041002
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.7184830979471516
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.728440040783058
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.7184567137414372
name: Spearman Euclidean
- type: pearson_dot
value: 0.7179974067629491
name: Pearson Dot
- type: spearman_dot
value: 0.7184567137414372
name: Spearman Dot
- type: pearson_max
value: 0.7285069661041002
name: Pearson Max
- type: spearman_max
value: 0.7184830979471516
name: Spearman Max
---
# SentenceTransformer based on lemon-mint/mMiniLMv2-L12-H384-Distilled-Iter12-final
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [lemon-mint/mMiniLMv2-L12-H384-Distilled-Iter12-final](https://huggingface.co/lemon-mint/mMiniLMv2-L12-H384-Distilled-Iter12-final). It maps sentences & paragraphs to a 384-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:** [lemon-mint/mMiniLMv2-L12-H384-Distilled-Iter12-final](https://huggingface.co/lemon-mint/mMiniLMv2-L12-H384-Distilled-Iter12-final)
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 384 tokens
- **Similarity Function:** Cosine Similarity
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: XLMRobertaModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(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("lemon-mint/mMiniLMv2-L12-H384-Distilled-Iter14-final")
# Run inference
sentences = [
'passage: British Rail produced a variety of railbuses, both as a means of acquiring new rolling stock cheaply, and to provide economical services on lightly-used lines. \n\nRailbuses are a very lightweight type of railcar designed specifically for passenger transport on little-used railway lines. As the name suggests, they share many aspects of their construction with a bus, usually having a bus body, or a modified bus body, and having four wheels on a fixed wheelbase, rather than bogies. Some units were equipped for operation as diesel multiple units.\n\nIn the late 1950s, British Rail tested a series of small railbuses, produced by a variety of manufacturers, for about £12,500 each (£261,000 at 2014 prices). These proved to be very economical (on test the Wickham bus was about ), but were somewhat unreliable. Most of the lines they worked on were closed following the Beeching Cuts and, being non-standard, they were all withdrawn in the mid-1960s, so they were never classified under the TOPS system.',
'query: What aircraft were evaluated under the Advanced Tanker Cargo Aircraft Program?',
'query: What do the results of the 2015 Ogun State House of Assembly election reveal about the political landscape of Ogun State?',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
## Evaluation
### Metrics
#### Semantic Similarity
* Dataset: `sts-dev`
* Evaluated with [EmbeddingSimilarityEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.7886 |
| **spearman_cosine** | **0.7955** |
| pearson_manhattan | 0.793 |
| spearman_manhattan | 0.7947 |
| pearson_euclidean | 0.7937 |
| spearman_euclidean | 0.7955 |
| pearson_dot | 0.7886 |
| spearman_dot | 0.7955 |
| pearson_max | 0.7937 |
| spearman_max | 0.7955 |
#### Information Retrieval
* Dataset: `Ko-StrategyQA-dev`
* Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.4932 |
| cosine_accuracy@3 | 0.6334 |
| cosine_accuracy@5 | 0.6774 |
| cosine_accuracy@10 | 0.7399 |
| cosine_precision@1 | 0.4932 |
| cosine_precision@3 | 0.2843 |
| cosine_precision@5 | 0.1986 |
| cosine_precision@10 | 0.1147 |
| cosine_recall@1 | 0.3179 |
| cosine_recall@3 | 0.4988 |
| cosine_recall@5 | 0.5588 |
| cosine_recall@10 | 0.6419 |
| cosine_ndcg@10 | 0.5481 |
| cosine_mrr@10 | 0.5747 |
| **cosine_map@100** | **0.4962** |
| dot_accuracy@1 | 0.4932 |
| dot_accuracy@3 | 0.6334 |
| dot_accuracy@5 | 0.6774 |
| dot_accuracy@10 | 0.7399 |
| dot_precision@1 | 0.4932 |
| dot_precision@3 | 0.2843 |
| dot_precision@5 | 0.1986 |
| dot_precision@10 | 0.1147 |
| dot_recall@1 | 0.3179 |
| dot_recall@3 | 0.4988 |
| dot_recall@5 | 0.5588 |
| dot_recall@10 | 0.6419 |
| dot_ndcg@10 | 0.5481 |
| dot_mrr@10 | 0.5747 |
| dot_map@100 | 0.4962 |
#### Semantic Similarity
* Dataset: `sts-test`
* Evaluated with [EmbeddingSimilarityEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.718 |
| **spearman_cosine** | **0.7185** |
| pearson_manhattan | 0.7285 |
| spearman_manhattan | 0.7185 |
| pearson_euclidean | 0.7284 |
| spearman_euclidean | 0.7185 |
| pearson_dot | 0.718 |
| spearman_dot | 0.7185 |
| pearson_max | 0.7285 |
| spearman_max | 0.7185 |
## Training Details
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 52
- `per_device_eval_batch_size`: 4
- `learning_rate`: 0.0001
- `num_train_epochs`: 1
- `warmup_ratio`: 0.05
- `fp16`: True
- `push_to_hub`: True
- `hub_model_id`: lemon-mint/mMiniLMv2-L12-H384-Distilled-Iter14
- `hub_strategy`: checkpoint
- `hub_private_repo`: True
#### All Hyperparameters
Click to expand
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 52
- `per_device_eval_batch_size`: 4
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `learning_rate`: 0.0001
- `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`: 0.05
- `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`: False
- `fp16`: True
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: True
- `resume_from_checkpoint`: None
- `hub_model_id`: lemon-mint/mMiniLMv2-L12-H384-Distilled-Iter14
- `hub_strategy`: checkpoint
- `hub_private_repo`: True
- `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
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional
Click to expand
| Epoch | Step | Training Loss | loss | Ko-StrategyQA-dev_cosine_map@100 | sts-dev_spearman_cosine | sts-test_spearman_cosine |
|:------:|:----:|:-------------:|:------:|:--------------------------------:|:-----------------------:|:------------------------:|
| 0 | 0 | - | - | 0.4817 | 0.7919 | - |
| 0.0011 | 10 | 0.0007 | - | - | - | - |
| 0.0022 | 20 | 0.0007 | - | - | - | - |
| 0.0032 | 30 | 0.0007 | - | - | - | - |
| 0.0043 | 40 | 0.0007 | - | - | - | - |
| 0.0054 | 50 | 0.0007 | - | - | - | - |
| 0.0065 | 60 | 0.0007 | - | - | - | - |
| 0.0076 | 70 | 0.0007 | - | - | - | - |
| 0.0087 | 80 | 0.0007 | - | - | - | - |
| 0.0097 | 90 | 0.0007 | - | - | - | - |
| 0.0108 | 100 | 0.0007 | - | - | - | - |
| 0.0119 | 110 | 0.0007 | - | - | - | - |
| 0.0130 | 120 | 0.0007 | - | - | - | - |
| 0.0141 | 130 | 0.0007 | - | - | - | - |
| 0.0151 | 140 | 0.0007 | - | - | - | - |
| 0.0162 | 150 | 0.0007 | - | - | - | - |
| 0.0173 | 160 | 0.0007 | - | - | - | - |
| 0.0184 | 170 | 0.0007 | - | - | - | - |
| 0.0195 | 180 | 0.0007 | - | - | - | - |
| 0.0206 | 190 | 0.0007 | - | - | - | - |
| 0.0216 | 200 | 0.0007 | - | - | - | - |
| 0.0227 | 210 | 0.0007 | - | - | - | - |
| 0.0238 | 220 | 0.0007 | - | - | - | - |
| 0.0249 | 230 | 0.0007 | - | - | - | - |
| 0.0260 | 240 | 0.0007 | - | - | - | - |
| 0.0270 | 250 | 0.0007 | - | - | - | - |
| 0.0281 | 260 | 0.0007 | - | - | - | - |
| 0.0292 | 270 | 0.0007 | - | - | - | - |
| 0.0303 | 280 | 0.0007 | - | - | - | - |
| 0.0314 | 290 | 0.0007 | - | - | - | - |
| 0.0325 | 300 | 0.0007 | - | - | - | - |
| 0.0335 | 310 | 0.0007 | - | - | - | - |
| 0.0346 | 320 | 0.0007 | - | - | - | - |
| 0.0357 | 330 | 0.0007 | - | - | - | - |
| 0.0368 | 340 | 0.0007 | - | - | - | - |
| 0.0379 | 350 | 0.0007 | - | - | - | - |
| 0.0389 | 360 | 0.0007 | - | - | - | - |
| 0.0400 | 370 | 0.0007 | - | - | - | - |
| 0.0411 | 380 | 0.0007 | - | - | - | - |
| 0.0422 | 390 | 0.0007 | - | - | - | - |
| 0.0433 | 400 | 0.0007 | - | - | - | - |
| 0.0444 | 410 | 0.0007 | - | - | - | - |
| 0.0454 | 420 | 0.0007 | - | - | - | - |
| 0.0465 | 430 | 0.0007 | - | - | - | - |
| 0.0476 | 440 | 0.0007 | - | - | - | - |
| 0.0487 | 450 | 0.0007 | - | - | - | - |
| 0.0498 | 460 | 0.0007 | - | - | - | - |
| 0.0508 | 470 | 0.0007 | - | - | - | - |
| 0.0519 | 480 | 0.0007 | - | - | - | - |
| 0.0530 | 490 | 0.0007 | - | - | - | - |
| 0.0541 | 500 | 0.0007 | - | - | - | - |
| 0.0552 | 510 | 0.0007 | - | - | - | - |
| 0.0563 | 520 | 0.0007 | - | - | - | - |
| 0.0573 | 530 | 0.0007 | - | - | - | - |
| 0.0584 | 540 | 0.0007 | - | - | - | - |
| 0.0595 | 550 | 0.0007 | - | - | - | - |
| 0.0606 | 560 | 0.0007 | - | - | - | - |
| 0.0617 | 570 | 0.0007 | - | - | - | - |
| 0.0628 | 580 | 0.0007 | - | - | - | - |
| 0.0638 | 590 | 0.0007 | - | - | - | - |
| 0.0649 | 600 | 0.0007 | - | - | - | - |
| 0.0660 | 610 | 0.0007 | - | - | - | - |
| 0.0671 | 620 | 0.0007 | - | - | - | - |
| 0.0682 | 630 | 0.0007 | - | - | - | - |
| 0.0692 | 640 | 0.0007 | - | - | - | - |
| 0.0703 | 650 | 0.0007 | - | - | - | - |
| 0.0714 | 660 | 0.0007 | - | - | - | - |
| 0.0725 | 670 | 0.0007 | - | - | - | - |
| 0.0736 | 680 | 0.0007 | - | - | - | - |
| 0.0747 | 690 | 0.0007 | - | - | - | - |
| 0.0757 | 700 | 0.0007 | - | - | - | - |
| 0.0768 | 710 | 0.0007 | - | - | - | - |
| 0.0779 | 720 | 0.0007 | - | - | - | - |
| 0.0790 | 730 | 0.0007 | - | - | - | - |
| 0.0801 | 740 | 0.0007 | - | - | - | - |
| 0.0811 | 750 | 0.0007 | - | - | - | - |
| 0.0822 | 760 | 0.0007 | - | - | - | - |
| 0.0833 | 770 | 0.0007 | - | - | - | - |
| 0.0844 | 780 | 0.0007 | - | - | - | - |
| 0.0855 | 790 | 0.0007 | - | - | - | - |
| 0.0866 | 800 | 0.0007 | - | - | - | - |
| 0.0876 | 810 | 0.0007 | - | - | - | - |
| 0.0887 | 820 | 0.0007 | - | - | - | - |
| 0.0898 | 830 | 0.0007 | - | - | - | - |
| 0.0909 | 840 | 0.0007 | - | - | - | - |
| 0.0920 | 850 | 0.0007 | - | - | - | - |
| 0.0930 | 860 | 0.0007 | - | - | - | - |
| 0.0941 | 870 | 0.0007 | - | - | - | - |
| 0.0952 | 880 | 0.0007 | - | - | - | - |
| 0.0963 | 890 | 0.0007 | - | - | - | - |
| 0.0974 | 900 | 0.0007 | - | - | - | - |
| 0.0985 | 910 | 0.0007 | - | - | - | - |
| 0.0995 | 920 | 0.0007 | - | - | - | - |
| 0.1006 | 930 | 0.0007 | - | - | - | - |
| 0.1017 | 940 | 0.0007 | - | - | - | - |
| 0.1028 | 950 | 0.0007 | - | - | - | - |
| 0.1039 | 960 | 0.0007 | - | - | - | - |
| 0.1049 | 970 | 0.0007 | - | - | - | - |
| 0.1060 | 980 | 0.0007 | - | - | - | - |
| 0.1071 | 990 | 0.0007 | - | - | - | - |
| 0.1082 | 1000 | 0.0007 | 0.0007 | 0.4708 | 0.7900 | - |
| 0.1093 | 1010 | 0.0007 | - | - | - | - |
| 0.1104 | 1020 | 0.0007 | - | - | - | - |
| 0.1114 | 1030 | 0.0007 | - | - | - | - |
| 0.1125 | 1040 | 0.0007 | - | - | - | - |
| 0.1136 | 1050 | 0.0007 | - | - | - | - |
| 0.1147 | 1060 | 0.0007 | - | - | - | - |
| 0.1158 | 1070 | 0.0007 | - | - | - | - |
| 0.1168 | 1080 | 0.0007 | - | - | - | - |
| 0.1179 | 1090 | 0.0007 | - | - | - | - |
| 0.1190 | 1100 | 0.0007 | - | - | - | - |
| 0.1201 | 1110 | 0.0007 | - | - | - | - |
| 0.1212 | 1120 | 0.0007 | - | - | - | - |
| 0.1223 | 1130 | 0.0007 | - | - | - | - |
| 0.1233 | 1140 | 0.0007 | - | - | - | - |
| 0.1244 | 1150 | 0.0007 | - | - | - | - |
| 0.1255 | 1160 | 0.0007 | - | - | - | - |
| 0.1266 | 1170 | 0.0007 | - | - | - | - |
| 0.1277 | 1180 | 0.0007 | - | - | - | - |
| 0.1287 | 1190 | 0.0007 | - | - | - | - |
| 0.1298 | 1200 | 0.0007 | - | - | - | - |
| 0.1309 | 1210 | 0.0007 | - | - | - | - |
| 0.1320 | 1220 | 0.0007 | - | - | - | - |
| 0.1331 | 1230 | 0.0007 | - | - | - | - |
| 0.1342 | 1240 | 0.0007 | - | - | - | - |
| 0.1352 | 1250 | 0.0007 | - | - | - | - |
| 0.1363 | 1260 | 0.0007 | - | - | - | - |
| 0.1374 | 1270 | 0.0007 | - | - | - | - |
| 0.1385 | 1280 | 0.0007 | - | - | - | - |
| 0.1396 | 1290 | 0.0007 | - | - | - | - |
| 0.1406 | 1300 | 0.0007 | - | - | - | - |
| 0.1417 | 1310 | 0.0007 | - | - | - | - |
| 0.1428 | 1320 | 0.0007 | - | - | - | - |
| 0.1439 | 1330 | 0.0007 | - | - | - | - |
| 0.1450 | 1340 | 0.0007 | - | - | - | - |
| 0.1461 | 1350 | 0.0007 | - | - | - | - |
| 0.1471 | 1360 | 0.0007 | - | - | - | - |
| 0.1482 | 1370 | 0.0007 | - | - | - | - |
| 0.1493 | 1380 | 0.0007 | - | - | - | - |
| 0.1504 | 1390 | 0.0007 | - | - | - | - |
| 0.1515 | 1400 | 0.0007 | - | - | - | - |
| 0.1525 | 1410 | 0.0007 | - | - | - | - |
| 0.1536 | 1420 | 0.0007 | - | - | - | - |
| 0.1547 | 1430 | 0.0007 | - | - | - | - |
| 0.1558 | 1440 | 0.0007 | - | - | - | - |
| 0.1569 | 1450 | 0.0007 | - | - | - | - |
| 0.1580 | 1460 | 0.0007 | - | - | - | - |
| 0.1590 | 1470 | 0.0007 | - | - | - | - |
| 0.1601 | 1480 | 0.0007 | - | - | - | - |
| 0.1612 | 1490 | 0.0007 | - | - | - | - |
| 0.1623 | 1500 | 0.0007 | - | - | - | - |
| 0.1634 | 1510 | 0.0007 | - | - | - | - |
| 0.1644 | 1520 | 0.0007 | - | - | - | - |
| 0.1655 | 1530 | 0.0007 | - | - | - | - |
| 0.1666 | 1540 | 0.0007 | - | - | - | - |
| 0.1677 | 1550 | 0.0007 | - | - | - | - |
| 0.1688 | 1560 | 0.0007 | - | - | - | - |
| 0.1699 | 1570 | 0.0007 | - | - | - | - |
| 0.1709 | 1580 | 0.0007 | - | - | - | - |
| 0.1720 | 1590 | 0.0007 | - | - | - | - |
| 0.1731 | 1600 | 0.0007 | - | - | - | - |
| 0.1742 | 1610 | 0.0007 | - | - | - | - |
| 0.1753 | 1620 | 0.0007 | - | - | - | - |
| 0.1763 | 1630 | 0.0007 | - | - | - | - |
| 0.1774 | 1640 | 0.0007 | - | - | - | - |
| 0.1785 | 1650 | 0.0007 | - | - | - | - |
| 0.1796 | 1660 | 0.0007 | - | - | - | - |
| 0.1807 | 1670 | 0.0007 | - | - | - | - |
| 0.1818 | 1680 | 0.0007 | - | - | - | - |
| 0.1828 | 1690 | 0.0007 | - | - | - | - |
| 0.1839 | 1700 | 0.0007 | - | - | - | - |
| 0.1850 | 1710 | 0.0007 | - | - | - | - |
| 0.1861 | 1720 | 0.0007 | - | - | - | - |
| 0.1872 | 1730 | 0.0007 | - | - | - | - |
| 0.1883 | 1740 | 0.0007 | - | - | - | - |
| 0.1893 | 1750 | 0.0007 | - | - | - | - |
| 0.1904 | 1760 | 0.0007 | - | - | - | - |
| 0.1915 | 1770 | 0.0007 | - | - | - | - |
| 0.1926 | 1780 | 0.0007 | - | - | - | - |
| 0.1937 | 1790 | 0.0007 | - | - | - | - |
| 0.1947 | 1800 | 0.0007 | - | - | - | - |
| 0.1958 | 1810 | 0.0007 | - | - | - | - |
| 0.1969 | 1820 | 0.0007 | - | - | - | - |
| 0.1980 | 1830 | 0.0007 | - | - | - | - |
| 0.1991 | 1840 | 0.0007 | - | - | - | - |
| 0.2002 | 1850 | 0.0007 | - | - | - | - |
| 0.2012 | 1860 | 0.0007 | - | - | - | - |
| 0.2023 | 1870 | 0.0007 | - | - | - | - |
| 0.2034 | 1880 | 0.0007 | - | - | - | - |
| 0.2045 | 1890 | 0.0007 | - | - | - | - |
| 0.2056 | 1900 | 0.0007 | - | - | - | - |
| 0.2066 | 1910 | 0.0007 | - | - | - | - |
| 0.2077 | 1920 | 0.0007 | - | - | - | - |
| 0.2088 | 1930 | 0.0007 | - | - | - | - |
| 0.2099 | 1940 | 0.0007 | - | - | - | - |
| 0.2110 | 1950 | 0.0007 | - | - | - | - |
| 0.2121 | 1960 | 0.0007 | - | - | - | - |
| 0.2131 | 1970 | 0.0007 | - | - | - | - |
| 0.2142 | 1980 | 0.0007 | - | - | - | - |
| 0.2153 | 1990 | 0.0007 | - | - | - | - |
| 0.2164 | 2000 | 0.0007 | 0.0007 | 0.4757 | 0.7863 | - |
| 0.2175 | 2010 | 0.0007 | - | - | - | - |
| 0.2185 | 2020 | 0.0007 | - | - | - | - |
| 0.2196 | 2030 | 0.0007 | - | - | - | - |
| 0.2207 | 2040 | 0.0007 | - | - | - | - |
| 0.2218 | 2050 | 0.0007 | - | - | - | - |
| 0.2229 | 2060 | 0.0007 | - | - | - | - |
| 0.2240 | 2070 | 0.0007 | - | - | - | - |
| 0.2250 | 2080 | 0.0007 | - | - | - | - |
| 0.2261 | 2090 | 0.0007 | - | - | - | - |
| 0.2272 | 2100 | 0.0007 | - | - | - | - |
| 0.2283 | 2110 | 0.0007 | - | - | - | - |
| 0.2294 | 2120 | 0.0007 | - | - | - | - |
| 0.2304 | 2130 | 0.0007 | - | - | - | - |
| 0.2315 | 2140 | 0.0007 | - | - | - | - |
| 0.2326 | 2150 | 0.0007 | - | - | - | - |
| 0.2337 | 2160 | 0.0007 | - | - | - | - |
| 0.2348 | 2170 | 0.0007 | - | - | - | - |
| 0.2359 | 2180 | 0.0007 | - | - | - | - |
| 0.2369 | 2190 | 0.0007 | - | - | - | - |
| 0.2380 | 2200 | 0.0007 | - | - | - | - |
| 0.2391 | 2210 | 0.0007 | - | - | - | - |
| 0.2402 | 2220 | 0.0007 | - | - | - | - |
| 0.2413 | 2230 | 0.0007 | - | - | - | - |
| 0.2423 | 2240 | 0.0007 | - | - | - | - |
| 0.2434 | 2250 | 0.0007 | - | - | - | - |
| 0.2445 | 2260 | 0.0007 | - | - | - | - |
| 0.2456 | 2270 | 0.0007 | - | - | - | - |
| 0.2467 | 2280 | 0.0007 | - | - | - | - |
| 0.2478 | 2290 | 0.0007 | - | - | - | - |
| 0.2488 | 2300 | 0.0007 | - | - | - | - |
| 0.2499 | 2310 | 0.0007 | - | - | - | - |
| 0.2510 | 2320 | 0.0007 | - | - | - | - |
| 0.2521 | 2330 | 0.0007 | - | - | - | - |
| 0.2532 | 2340 | 0.0007 | - | - | - | - |
| 0.2542 | 2350 | 0.0007 | - | - | - | - |
| 0.2553 | 2360 | 0.0007 | - | - | - | - |
| 0.2564 | 2370 | 0.0007 | - | - | - | - |
| 0.2575 | 2380 | 0.0007 | - | - | - | - |
| 0.2586 | 2390 | 0.0007 | - | - | - | - |
| 0.2597 | 2400 | 0.0007 | - | - | - | - |
| 0.2607 | 2410 | 0.0007 | - | - | - | - |
| 0.2618 | 2420 | 0.0007 | - | - | - | - |
| 0.2629 | 2430 | 0.0007 | - | - | - | - |
| 0.2640 | 2440 | 0.0007 | - | - | - | - |
| 0.2651 | 2450 | 0.0007 | - | - | - | - |
| 0.2661 | 2460 | 0.0007 | - | - | - | - |
| 0.2672 | 2470 | 0.0007 | - | - | - | - |
| 0.2683 | 2480 | 0.0007 | - | - | - | - |
| 0.2694 | 2490 | 0.0007 | - | - | - | - |
| 0.2705 | 2500 | 0.0007 | - | - | - | - |
| 0.2716 | 2510 | 0.0007 | - | - | - | - |
| 0.2726 | 2520 | 0.0007 | - | - | - | - |
| 0.2737 | 2530 | 0.0007 | - | - | - | - |
| 0.2748 | 2540 | 0.0007 | - | - | - | - |
| 0.2759 | 2550 | 0.0007 | - | - | - | - |
| 0.2770 | 2560 | 0.0007 | - | - | - | - |
| 0.2780 | 2570 | 0.0007 | - | - | - | - |
| 0.2791 | 2580 | 0.0007 | - | - | - | - |
| 0.2802 | 2590 | 0.0007 | - | - | - | - |
| 0.2813 | 2600 | 0.0007 | - | - | - | - |
| 0.2824 | 2610 | 0.0007 | - | - | - | - |
| 0.2835 | 2620 | 0.0007 | - | - | - | - |
| 0.2845 | 2630 | 0.0007 | - | - | - | - |
| 0.2856 | 2640 | 0.0007 | - | - | - | - |
| 0.2867 | 2650 | 0.0007 | - | - | - | - |
| 0.2878 | 2660 | 0.0007 | - | - | - | - |
| 0.2889 | 2670 | 0.0007 | - | - | - | - |
| 0.2899 | 2680 | 0.0007 | - | - | - | - |
| 0.2910 | 2690 | 0.0007 | - | - | - | - |
| 0.2921 | 2700 | 0.0007 | - | - | - | - |
| 0.2932 | 2710 | 0.0007 | - | - | - | - |
| 0.2943 | 2720 | 0.0007 | - | - | - | - |
| 0.2954 | 2730 | 0.0007 | - | - | - | - |
| 0.2964 | 2740 | 0.0007 | - | - | - | - |
| 0.2975 | 2750 | 0.0007 | - | - | - | - |
| 0.2986 | 2760 | 0.0007 | - | - | - | - |
| 0.2997 | 2770 | 0.0007 | - | - | - | - |
| 0.3008 | 2780 | 0.0007 | - | - | - | - |
| 0.3019 | 2790 | 0.0007 | - | - | - | - |
| 0.3029 | 2800 | 0.0007 | - | - | - | - |
| 0.3040 | 2810 | 0.0007 | - | - | - | - |
| 0.3051 | 2820 | 0.0007 | - | - | - | - |
| 0.3062 | 2830 | 0.0007 | - | - | - | - |
| 0.3073 | 2840 | 0.0007 | - | - | - | - |
| 0.3083 | 2850 | 0.0007 | - | - | - | - |
| 0.3094 | 2860 | 0.0007 | - | - | - | - |
| 0.3105 | 2870 | 0.0007 | - | - | - | - |
| 0.3116 | 2880 | 0.0007 | - | - | - | - |
| 0.3127 | 2890 | 0.0007 | - | - | - | - |
| 0.3138 | 2900 | 0.0007 | - | - | - | - |
| 0.3148 | 2910 | 0.0007 | - | - | - | - |
| 0.3159 | 2920 | 0.0007 | - | - | - | - |
| 0.3170 | 2930 | 0.0007 | - | - | - | - |
| 0.3181 | 2940 | 0.0007 | - | - | - | - |
| 0.3192 | 2950 | 0.0007 | - | - | - | - |
| 0.3202 | 2960 | 0.0007 | - | - | - | - |
| 0.3213 | 2970 | 0.0007 | - | - | - | - |
| 0.3224 | 2980 | 0.0007 | - | - | - | - |
| 0.3235 | 2990 | 0.0007 | - | - | - | - |
| 0.3246 | 3000 | 0.0007 | 0.0007 | 0.4816 | 0.7941 | - |
| 0.3257 | 3010 | 0.0007 | - | - | - | - |
| 0.3267 | 3020 | 0.0007 | - | - | - | - |
| 0.3278 | 3030 | 0.0007 | - | - | - | - |
| 0.3289 | 3040 | 0.0007 | - | - | - | - |
| 0.3300 | 3050 | 0.0007 | - | - | - | - |
| 0.3311 | 3060 | 0.0007 | - | - | - | - |
| 0.3321 | 3070 | 0.0007 | - | - | - | - |
| 0.3332 | 3080 | 0.0007 | - | - | - | - |
| 0.3343 | 3090 | 0.0007 | - | - | - | - |
| 0.3354 | 3100 | 0.0007 | - | - | - | - |
| 0.3365 | 3110 | 0.0007 | - | - | - | - |
| 0.3376 | 3120 | 0.0007 | - | - | - | - |
| 0.3386 | 3130 | 0.0007 | - | - | - | - |
| 0.3397 | 3140 | 0.0007 | - | - | - | - |
| 0.3408 | 3150 | 0.0007 | - | - | - | - |
| 0.3419 | 3160 | 0.0007 | - | - | - | - |
| 0.3430 | 3170 | 0.0007 | - | - | - | - |
| 0.3440 | 3180 | 0.0007 | - | - | - | - |
| 0.3451 | 3190 | 0.0007 | - | - | - | - |
| 0.3462 | 3200 | 0.0007 | - | - | - | - |
| 0.3473 | 3210 | 0.0007 | - | - | - | - |
| 0.3484 | 3220 | 0.0007 | - | - | - | - |
| 0.3495 | 3230 | 0.0007 | - | - | - | - |
| 0.3505 | 3240 | 0.0007 | - | - | - | - |
| 0.3516 | 3250 | 0.0007 | - | - | - | - |
| 0.3527 | 3260 | 0.0007 | - | - | - | - |
| 0.3538 | 3270 | 0.0007 | - | - | - | - |
| 0.3549 | 3280 | 0.0007 | - | - | - | - |
| 0.3559 | 3290 | 0.0007 | - | - | - | - |
| 0.3570 | 3300 | 0.0007 | - | - | - | - |
| 0.3581 | 3310 | 0.0007 | - | - | - | - |
| 0.3592 | 3320 | 0.0007 | - | - | - | - |
| 0.3603 | 3330 | 0.0007 | - | - | - | - |
| 0.3614 | 3340 | 0.0007 | - | - | - | - |
| 0.3624 | 3350 | 0.0007 | - | - | - | - |
| 0.3635 | 3360 | 0.0007 | - | - | - | - |
| 0.3646 | 3370 | 0.0007 | - | - | - | - |
| 0.3657 | 3380 | 0.0007 | - | - | - | - |
| 0.3668 | 3390 | 0.0007 | - | - | - | - |
| 0.3678 | 3400 | 0.0007 | - | - | - | - |
| 0.3689 | 3410 | 0.0007 | - | - | - | - |
| 0.3700 | 3420 | 0.0007 | - | - | - | - |
| 0.3711 | 3430 | 0.0007 | - | - | - | - |
| 0.3722 | 3440 | 0.0007 | - | - | - | - |
| 0.3733 | 3450 | 0.0007 | - | - | - | - |
| 0.3743 | 3460 | 0.0007 | - | - | - | - |
| 0.3754 | 3470 | 0.0007 | - | - | - | - |
| 0.3765 | 3480 | 0.0007 | - | - | - | - |
| 0.3776 | 3490 | 0.0007 | - | - | - | - |
| 0.3787 | 3500 | 0.0007 | - | - | - | - |
| 0.3797 | 3510 | 0.0007 | - | - | - | - |
| 0.3808 | 3520 | 0.0007 | - | - | - | - |
| 0.3819 | 3530 | 0.0007 | - | - | - | - |
| 0.3830 | 3540 | 0.0007 | - | - | - | - |
| 0.3841 | 3550 | 0.0007 | - | - | - | - |
| 0.3852 | 3560 | 0.0007 | - | - | - | - |
| 0.3862 | 3570 | 0.0007 | - | - | - | - |
| 0.3873 | 3580 | 0.0007 | - | - | - | - |
| 0.3884 | 3590 | 0.0007 | - | - | - | - |
| 0.3895 | 3600 | 0.0007 | - | - | - | - |
| 0.3906 | 3610 | 0.0007 | - | - | - | - |
| 0.3916 | 3620 | 0.0007 | - | - | - | - |
| 0.3927 | 3630 | 0.0007 | - | - | - | - |
| 0.3938 | 3640 | 0.0007 | - | - | - | - |
| 0.3949 | 3650 | 0.0007 | - | - | - | - |
| 0.3960 | 3660 | 0.0007 | - | - | - | - |
| 0.3971 | 3670 | 0.0007 | - | - | - | - |
| 0.3981 | 3680 | 0.0007 | - | - | - | - |
| 0.3992 | 3690 | 0.0007 | - | - | - | - |
| 0.4003 | 3700 | 0.0007 | - | - | - | - |
| 0.4014 | 3710 | 0.0007 | - | - | - | - |
| 0.4025 | 3720 | 0.0007 | - | - | - | - |
| 0.4035 | 3730 | 0.0007 | - | - | - | - |
| 0.4046 | 3740 | 0.0007 | - | - | - | - |
| 0.4057 | 3750 | 0.0007 | - | - | - | - |
| 0.4068 | 3760 | 0.0007 | - | - | - | - |
| 0.4079 | 3770 | 0.0007 | - | - | - | - |
| 0.4090 | 3780 | 0.0007 | - | - | - | - |
| 0.4100 | 3790 | 0.0007 | - | - | - | - |
| 0.4111 | 3800 | 0.0007 | - | - | - | - |
| 0.4122 | 3810 | 0.0007 | - | - | - | - |
| 0.4133 | 3820 | 0.0007 | - | - | - | - |
| 0.4144 | 3830 | 0.0007 | - | - | - | - |
| 0.4154 | 3840 | 0.0007 | - | - | - | - |
| 0.4165 | 3850 | 0.0007 | - | - | - | - |
| 0.4176 | 3860 | 0.0007 | - | - | - | - |
| 0.4187 | 3870 | 0.0007 | - | - | - | - |
| 0.4198 | 3880 | 0.0007 | - | - | - | - |
| 0.4209 | 3890 | 0.0007 | - | - | - | - |
| 0.4219 | 3900 | 0.0007 | - | - | - | - |
| 0.4230 | 3910 | 0.0007 | - | - | - | - |
| 0.4241 | 3920 | 0.0007 | - | - | - | - |
| 0.4252 | 3930 | 0.0007 | - | - | - | - |
| 0.4263 | 3940 | 0.0007 | - | - | - | - |
| 0.4274 | 3950 | 0.0007 | - | - | - | - |
| 0.4284 | 3960 | 0.0007 | - | - | - | - |
| 0.4295 | 3970 | 0.0007 | - | - | - | - |
| 0.4306 | 3980 | 0.0007 | - | - | - | - |
| 0.4317 | 3990 | 0.0007 | - | - | - | - |
| 0.4328 | 4000 | 0.0007 | 0.0007 | 0.4878 | 0.7932 | - |
| 0.4338 | 4010 | 0.0007 | - | - | - | - |
| 0.4349 | 4020 | 0.0007 | - | - | - | - |
| 0.4360 | 4030 | 0.0007 | - | - | - | - |
| 0.4371 | 4040 | 0.0007 | - | - | - | - |
| 0.4382 | 4050 | 0.0007 | - | - | - | - |
| 0.4393 | 4060 | 0.0007 | - | - | - | - |
| 0.4403 | 4070 | 0.0007 | - | - | - | - |
| 0.4414 | 4080 | 0.0007 | - | - | - | - |
| 0.4425 | 4090 | 0.0007 | - | - | - | - |
| 0.4436 | 4100 | 0.0007 | - | - | - | - |
| 0.4447 | 4110 | 0.0007 | - | - | - | - |
| 0.4457 | 4120 | 0.0007 | - | - | - | - |
| 0.4468 | 4130 | 0.0007 | - | - | - | - |
| 0.4479 | 4140 | 0.0007 | - | - | - | - |
| 0.4490 | 4150 | 0.0007 | - | - | - | - |
| 0.4501 | 4160 | 0.0007 | - | - | - | - |
| 0.4512 | 4170 | 0.0007 | - | - | - | - |
| 0.4522 | 4180 | 0.0007 | - | - | - | - |
| 0.4533 | 4190 | 0.0007 | - | - | - | - |
| 0.4544 | 4200 | 0.0007 | - | - | - | - |
| 0.4555 | 4210 | 0.0007 | - | - | - | - |
| 0.4566 | 4220 | 0.0007 | - | - | - | - |
| 0.4576 | 4230 | 0.0007 | - | - | - | - |
| 0.4587 | 4240 | 0.0007 | - | - | - | - |
| 0.4598 | 4250 | 0.0006 | - | - | - | - |
| 0.4609 | 4260 | 0.0007 | - | - | - | - |
| 0.4620 | 4270 | 0.0007 | - | - | - | - |
| 0.4631 | 4280 | 0.0007 | - | - | - | - |
| 0.4641 | 4290 | 0.0007 | - | - | - | - |
| 0.4652 | 4300 | 0.0007 | - | - | - | - |
| 0.4663 | 4310 | 0.0007 | - | - | - | - |
| 0.4674 | 4320 | 0.0007 | - | - | - | - |
| 0.4685 | 4330 | 0.0007 | - | - | - | - |
| 0.4695 | 4340 | 0.0007 | - | - | - | - |
| 0.4706 | 4350 | 0.0007 | - | - | - | - |
| 0.4717 | 4360 | 0.0007 | - | - | - | - |
| 0.4728 | 4370 | 0.0007 | - | - | - | - |
| 0.4739 | 4380 | 0.0007 | - | - | - | - |
| 0.4750 | 4390 | 0.0007 | - | - | - | - |
| 0.4760 | 4400 | 0.0007 | - | - | - | - |
| 0.4771 | 4410 | 0.0007 | - | - | - | - |
| 0.4782 | 4420 | 0.0007 | - | - | - | - |
| 0.4793 | 4430 | 0.0007 | - | - | - | - |
| 0.4804 | 4440 | 0.0007 | - | - | - | - |
| 0.4814 | 4450 | 0.0007 | - | - | - | - |
| 0.4825 | 4460 | 0.0007 | - | - | - | - |
| 0.4836 | 4470 | 0.0007 | - | - | - | - |
| 0.4847 | 4480 | 0.0007 | - | - | - | - |
| 0.4858 | 4490 | 0.0007 | - | - | - | - |
| 0.4869 | 4500 | 0.0007 | - | - | - | - |
| 0.4879 | 4510 | 0.0007 | - | - | - | - |
| 0.4890 | 4520 | 0.0007 | - | - | - | - |
| 0.4901 | 4530 | 0.0007 | - | - | - | - |
| 0.4912 | 4540 | 0.0007 | - | - | - | - |
| 0.4923 | 4550 | 0.0007 | - | - | - | - |
| 0.4933 | 4560 | 0.0007 | - | - | - | - |
| 0.4944 | 4570 | 0.0007 | - | - | - | - |
| 0.4955 | 4580 | 0.0007 | - | - | - | - |
| 0.4966 | 4590 | 0.0007 | - | - | - | - |
| 0.4977 | 4600 | 0.0007 | - | - | - | - |
| 0.4988 | 4610 | 0.0007 | - | - | - | - |
| 0.4998 | 4620 | 0.0007 | - | - | - | - |
| 0.5009 | 4630 | 0.0007 | - | - | - | - |
| 0.5020 | 4640 | 0.0007 | - | - | - | - |
| 0.5031 | 4650 | 0.0007 | - | - | - | - |
| 0.5042 | 4660 | 0.0007 | - | - | - | - |
| 0.5052 | 4670 | 0.0007 | - | - | - | - |
| 0.5063 | 4680 | 0.0007 | - | - | - | - |
| 0.5074 | 4690 | 0.0007 | - | - | - | - |
| 0.5085 | 4700 | 0.0007 | - | - | - | - |
| 0.5096 | 4710 | 0.0007 | - | - | - | - |
| 0.5107 | 4720 | 0.0007 | - | - | - | - |
| 0.5117 | 4730 | 0.0007 | - | - | - | - |
| 0.5128 | 4740 | 0.0007 | - | - | - | - |
| 0.5139 | 4750 | 0.0007 | - | - | - | - |
| 0.5150 | 4760 | 0.0007 | - | - | - | - |
| 0.5161 | 4770 | 0.0007 | - | - | - | - |
| 0.5171 | 4780 | 0.0007 | - | - | - | - |
| 0.5182 | 4790 | 0.0007 | - | - | - | - |
| 0.5193 | 4800 | 0.0007 | - | - | - | - |
| 0.5204 | 4810 | 0.0007 | - | - | - | - |
| 0.5215 | 4820 | 0.0007 | - | - | - | - |
| 0.5226 | 4830 | 0.0006 | - | - | - | - |
| 0.5236 | 4840 | 0.0007 | - | - | - | - |
| 0.5247 | 4850 | 0.0007 | - | - | - | - |
| 0.5258 | 4860 | 0.0007 | - | - | - | - |
| 0.5269 | 4870 | 0.0007 | - | - | - | - |
| 0.5280 | 4880 | 0.0007 | - | - | - | - |
| 0.5290 | 4890 | 0.0007 | - | - | - | - |
| 0.5301 | 4900 | 0.0007 | - | - | - | - |
| 0.5312 | 4910 | 0.0007 | - | - | - | - |
| 0.5323 | 4920 | 0.0007 | - | - | - | - |
| 0.5334 | 4930 | 0.0007 | - | - | - | - |
| 0.5345 | 4940 | 0.0007 | - | - | - | - |
| 0.5355 | 4950 | 0.0007 | - | - | - | - |
| 0.5366 | 4960 | 0.0007 | - | - | - | - |
| 0.5377 | 4970 | 0.0007 | - | - | - | - |
| 0.5388 | 4980 | 0.0007 | - | - | - | - |
| 0.5399 | 4990 | 0.0007 | - | - | - | - |
| 0.5409 | 5000 | 0.0007 | 0.0006 | 0.4867 | 0.7951 | - |
| 0.5420 | 5010 | 0.0007 | - | - | - | - |
| 0.5431 | 5020 | 0.0007 | - | - | - | - |
| 0.5442 | 5030 | 0.0007 | - | - | - | - |
| 0.5453 | 5040 | 0.0007 | - | - | - | - |
| 0.5464 | 5050 | 0.0007 | - | - | - | - |
| 0.5474 | 5060 | 0.0007 | - | - | - | - |
| 0.5485 | 5070 | 0.0007 | - | - | - | - |
| 0.5496 | 5080 | 0.0007 | - | - | - | - |
| 0.5507 | 5090 | 0.0007 | - | - | - | - |
| 0.5518 | 5100 | 0.0006 | - | - | - | - |
| 0.5529 | 5110 | 0.0007 | - | - | - | - |
| 0.5539 | 5120 | 0.0007 | - | - | - | - |
| 0.5550 | 5130 | 0.0007 | - | - | - | - |
| 0.5561 | 5140 | 0.0007 | - | - | - | - |
| 0.5572 | 5150 | 0.0007 | - | - | - | - |
| 0.5583 | 5160 | 0.0007 | - | - | - | - |
| 0.5593 | 5170 | 0.0007 | - | - | - | - |
| 0.5604 | 5180 | 0.0007 | - | - | - | - |
| 0.5615 | 5190 | 0.0007 | - | - | - | - |
| 0.5626 | 5200 | 0.0007 | - | - | - | - |
| 0.5637 | 5210 | 0.0007 | - | - | - | - |
| 0.5648 | 5220 | 0.0007 | - | - | - | - |
| 0.5658 | 5230 | 0.0007 | - | - | - | - |
| 0.5669 | 5240 | 0.0007 | - | - | - | - |
| 0.5680 | 5250 | 0.0007 | - | - | - | - |
| 0.5691 | 5260 | 0.0007 | - | - | - | - |
| 0.5702 | 5270 | 0.0007 | - | - | - | - |
| 0.5712 | 5280 | 0.0007 | - | - | - | - |
| 0.5723 | 5290 | 0.0007 | - | - | - | - |
| 0.5734 | 5300 | 0.0007 | - | - | - | - |
| 0.5745 | 5310 | 0.0007 | - | - | - | - |
| 0.5756 | 5320 | 0.0007 | - | - | - | - |
| 0.5767 | 5330 | 0.0007 | - | - | - | - |
| 0.5777 | 5340 | 0.0006 | - | - | - | - |
| 0.5788 | 5350 | 0.0007 | - | - | - | - |
| 0.5799 | 5360 | 0.0007 | - | - | - | - |
| 0.5810 | 5370 | 0.0007 | - | - | - | - |
| 0.5821 | 5380 | 0.0006 | - | - | - | - |
| 0.5831 | 5390 | 0.0007 | - | - | - | - |
| 0.5842 | 5400 | 0.0007 | - | - | - | - |
| 0.5853 | 5410 | 0.0007 | - | - | - | - |
| 0.5864 | 5420 | 0.0007 | - | - | - | - |
| 0.5875 | 5430 | 0.0007 | - | - | - | - |
| 0.5886 | 5440 | 0.0007 | - | - | - | - |
| 0.5896 | 5450 | 0.0006 | - | - | - | - |
| 0.5907 | 5460 | 0.0007 | - | - | - | - |
| 0.5918 | 5470 | 0.0007 | - | - | - | - |
| 0.5929 | 5480 | 0.0007 | - | - | - | - |
| 0.5940 | 5490 | 0.0007 | - | - | - | - |
| 0.5950 | 5500 | 0.0007 | - | - | - | - |
| 0.5961 | 5510 | 0.0007 | - | - | - | - |
| 0.5972 | 5520 | 0.0007 | - | - | - | - |
| 0.5983 | 5530 | 0.0007 | - | - | - | - |
| 0.5994 | 5540 | 0.0007 | - | - | - | - |
| 0.6005 | 5550 | 0.0007 | - | - | - | - |
| 0.6015 | 5560 | 0.0007 | - | - | - | - |
| 0.6026 | 5570 | 0.0007 | - | - | - | - |
| 0.6037 | 5580 | 0.0007 | - | - | - | - |
| 0.6048 | 5590 | 0.0007 | - | - | - | - |
| 0.6059 | 5600 | 0.0007 | - | - | - | - |
| 0.6069 | 5610 | 0.0007 | - | - | - | - |
| 0.6080 | 5620 | 0.0007 | - | - | - | - |
| 0.6091 | 5630 | 0.0007 | - | - | - | - |
| 0.6102 | 5640 | 0.0007 | - | - | - | - |
| 0.6113 | 5650 | 0.0007 | - | - | - | - |
| 0.6124 | 5660 | 0.0007 | - | - | - | - |
| 0.6134 | 5670 | 0.0007 | - | - | - | - |
| 0.6145 | 5680 | 0.0007 | - | - | - | - |
| 0.6156 | 5690 | 0.0007 | - | - | - | - |
| 0.6167 | 5700 | 0.0007 | - | - | - | - |
| 0.6178 | 5710 | 0.0007 | - | - | - | - |
| 0.6188 | 5720 | 0.0007 | - | - | - | - |
| 0.6199 | 5730 | 0.0007 | - | - | - | - |
| 0.6210 | 5740 | 0.0007 | - | - | - | - |
| 0.6221 | 5750 | 0.0007 | - | - | - | - |
| 0.6232 | 5760 | 0.0007 | - | - | - | - |
| 0.6243 | 5770 | 0.0007 | - | - | - | - |
| 0.6253 | 5780 | 0.0007 | - | - | - | - |
| 0.6264 | 5790 | 0.0007 | - | - | - | - |
| 0.6275 | 5800 | 0.0007 | - | - | - | - |
| 0.6286 | 5810 | 0.0007 | - | - | - | - |
| 0.6297 | 5820 | 0.0007 | - | - | - | - |
| 0.6307 | 5830 | 0.0007 | - | - | - | - |
| 0.6318 | 5840 | 0.0007 | - | - | - | - |
| 0.6329 | 5850 | 0.0007 | - | - | - | - |
| 0.6340 | 5860 | 0.0007 | - | - | - | - |
| 0.6351 | 5870 | 0.0007 | - | - | - | - |
| 0.6362 | 5880 | 0.0007 | - | - | - | - |
| 0.6372 | 5890 | 0.0006 | - | - | - | - |
| 0.6383 | 5900 | 0.0006 | - | - | - | - |
| 0.6394 | 5910 | 0.0007 | - | - | - | - |
| 0.6405 | 5920 | 0.0007 | - | - | - | - |
| 0.6416 | 5930 | 0.0007 | - | - | - | - |
| 0.6426 | 5940 | 0.0007 | - | - | - | - |
| 0.6437 | 5950 | 0.0007 | - | - | - | - |
| 0.6448 | 5960 | 0.0007 | - | - | - | - |
| 0.6459 | 5970 | 0.0007 | - | - | - | - |
| 0.6470 | 5980 | 0.0007 | - | - | - | - |
| 0.6481 | 5990 | 0.0007 | - | - | - | - |
| 0.6491 | 6000 | 0.0007 | 0.0006 | 0.4981 | 0.7961 | - |
| 0.6502 | 6010 | 0.0006 | - | - | - | - |
| 0.6513 | 6020 | 0.0007 | - | - | - | - |
| 0.6524 | 6030 | 0.0007 | - | - | - | - |
| 0.6535 | 6040 | 0.0007 | - | - | - | - |
| 0.6545 | 6050 | 0.0007 | - | - | - | - |
| 0.6556 | 6060 | 0.0007 | - | - | - | - |
| 0.6567 | 6070 | 0.0007 | - | - | - | - |
| 0.6578 | 6080 | 0.0007 | - | - | - | - |
| 0.6589 | 6090 | 0.0007 | - | - | - | - |
| 0.6600 | 6100 | 0.0007 | - | - | - | - |
| 0.6610 | 6110 | 0.0007 | - | - | - | - |
| 0.6621 | 6120 | 0.0007 | - | - | - | - |
| 0.6632 | 6130 | 0.0007 | - | - | - | - |
| 0.6643 | 6140 | 0.0007 | - | - | - | - |
| 0.6654 | 6150 | 0.0007 | - | - | - | - |
| 0.6665 | 6160 | 0.0007 | - | - | - | - |
| 0.6675 | 6170 | 0.0006 | - | - | - | - |
| 0.6686 | 6180 | 0.0007 | - | - | - | - |
| 0.6697 | 6190 | 0.0007 | - | - | - | - |
| 0.6708 | 6200 | 0.0007 | - | - | - | - |
| 0.6719 | 6210 | 0.0007 | - | - | - | - |
| 0.6729 | 6220 | 0.0007 | - | - | - | - |
| 0.6740 | 6230 | 0.0007 | - | - | - | - |
| 0.6751 | 6240 | 0.0007 | - | - | - | - |
| 0.6762 | 6250 | 0.0007 | - | - | - | - |
| 0.6773 | 6260 | 0.0007 | - | - | - | - |
| 0.6784 | 6270 | 0.0007 | - | - | - | - |
| 0.6794 | 6280 | 0.0007 | - | - | - | - |
| 0.6805 | 6290 | 0.0007 | - | - | - | - |
| 0.6816 | 6300 | 0.0007 | - | - | - | - |
| 0.6827 | 6310 | 0.0007 | - | - | - | - |
| 0.6838 | 6320 | 0.0007 | - | - | - | - |
| 0.6848 | 6330 | 0.0007 | - | - | - | - |
| 0.6859 | 6340 | 0.0007 | - | - | - | - |
| 0.6870 | 6350 | 0.0007 | - | - | - | - |
| 0.6881 | 6360 | 0.0007 | - | - | - | - |
| 0.6892 | 6370 | 0.0007 | - | - | - | - |
| 0.6903 | 6380 | 0.0007 | - | - | - | - |
| 0.6913 | 6390 | 0.0007 | - | - | - | - |
| 0.6924 | 6400 | 0.0007 | - | - | - | - |
| 0.6935 | 6410 | 0.0007 | - | - | - | - |
| 0.6946 | 6420 | 0.0006 | - | - | - | - |
| 0.6957 | 6430 | 0.0006 | - | - | - | - |
| 0.6967 | 6440 | 0.0007 | - | - | - | - |
| 0.6978 | 6450 | 0.0006 | - | - | - | - |
| 0.6989 | 6460 | 0.0007 | - | - | - | - |
| 0.7000 | 6470 | 0.0007 | - | - | - | - |
| 0.7011 | 6480 | 0.0006 | - | - | - | - |
| 0.7022 | 6490 | 0.0007 | - | - | - | - |
| 0.7032 | 6500 | 0.0006 | - | - | - | - |
| 0.7043 | 6510 | 0.0007 | - | - | - | - |
| 0.7054 | 6520 | 0.0007 | - | - | - | - |
| 0.7065 | 6530 | 0.0007 | - | - | - | - |
| 0.7076 | 6540 | 0.0007 | - | - | - | - |
| 0.7086 | 6550 | 0.0007 | - | - | - | - |
| 0.7097 | 6560 | 0.0007 | - | - | - | - |
| 0.7108 | 6570 | 0.0007 | - | - | - | - |
| 0.7119 | 6580 | 0.0007 | - | - | - | - |
| 0.7130 | 6590 | 0.0007 | - | - | - | - |
| 0.7141 | 6600 | 0.0007 | - | - | - | - |
| 0.7151 | 6610 | 0.0006 | - | - | - | - |
| 0.7162 | 6620 | 0.0007 | - | - | - | - |
| 0.7173 | 6630 | 0.0007 | - | - | - | - |
| 0.7184 | 6640 | 0.0007 | - | - | - | - |
| 0.7195 | 6650 | 0.0007 | - | - | - | - |
| 0.7205 | 6660 | 0.0007 | - | - | - | - |
| 0.7216 | 6670 | 0.0007 | - | - | - | - |
| 0.7227 | 6680 | 0.0007 | - | - | - | - |
| 0.7238 | 6690 | 0.0007 | - | - | - | - |
| 0.7249 | 6700 | 0.0007 | - | - | - | - |
| 0.7260 | 6710 | 0.0007 | - | - | - | - |
| 0.7270 | 6720 | 0.0007 | - | - | - | - |
| 0.7281 | 6730 | 0.0007 | - | - | - | - |
| 0.7292 | 6740 | 0.0007 | - | - | - | - |
| 0.7303 | 6750 | 0.0007 | - | - | - | - |
| 0.7314 | 6760 | 0.0006 | - | - | - | - |
| 0.7324 | 6770 | 0.0007 | - | - | - | - |
| 0.7335 | 6780 | 0.0007 | - | - | - | - |
| 0.7346 | 6790 | 0.0006 | - | - | - | - |
| 0.7357 | 6800 | 0.0007 | - | - | - | - |
| 0.7368 | 6810 | 0.0006 | - | - | - | - |
| 0.7379 | 6820 | 0.0006 | - | - | - | - |
| 0.7389 | 6830 | 0.0006 | - | - | - | - |
| 0.7400 | 6840 | 0.0007 | - | - | - | - |
| 0.7411 | 6850 | 0.0007 | - | - | - | - |
| 0.7422 | 6860 | 0.0007 | - | - | - | - |
| 0.7433 | 6870 | 0.0006 | - | - | - | - |
| 0.7443 | 6880 | 0.0007 | - | - | - | - |
| 0.7454 | 6890 | 0.0007 | - | - | - | - |
| 0.7465 | 6900 | 0.0007 | - | - | - | - |
| 0.7476 | 6910 | 0.0006 | - | - | - | - |
| 0.7487 | 6920 | 0.0007 | - | - | - | - |
| 0.7498 | 6930 | 0.0006 | - | - | - | - |
| 0.7508 | 6940 | 0.0007 | - | - | - | - |
| 0.7519 | 6950 | 0.0007 | - | - | - | - |
| 0.7530 | 6960 | 0.0007 | - | - | - | - |
| 0.7541 | 6970 | 0.0007 | - | - | - | - |
| 0.7552 | 6980 | 0.0007 | - | - | - | - |
| 0.7562 | 6990 | 0.0007 | - | - | - | - |
| 0.7573 | 7000 | 0.0006 | 0.0006 | 0.4935 | 0.7972 | - |
| 0.7584 | 7010 | 0.0007 | - | - | - | - |
| 0.7595 | 7020 | 0.0007 | - | - | - | - |
| 0.7606 | 7030 | 0.0007 | - | - | - | - |
| 0.7617 | 7040 | 0.0007 | - | - | - | - |
| 0.7627 | 7050 | 0.0007 | - | - | - | - |
| 0.7638 | 7060 | 0.0006 | - | - | - | - |
| 0.7649 | 7070 | 0.0007 | - | - | - | - |
| 0.7660 | 7080 | 0.0007 | - | - | - | - |
| 0.7671 | 7090 | 0.0007 | - | - | - | - |
| 0.7681 | 7100 | 0.0007 | - | - | - | - |
| 0.7692 | 7110 | 0.0007 | - | - | - | - |
| 0.7703 | 7120 | 0.0007 | - | - | - | - |
| 0.7714 | 7130 | 0.0007 | - | - | - | - |
| 0.7725 | 7140 | 0.0006 | - | - | - | - |
| 0.7736 | 7150 | 0.0006 | - | - | - | - |
| 0.7746 | 7160 | 0.0007 | - | - | - | - |
| 0.7757 | 7170 | 0.0006 | - | - | - | - |
| 0.7768 | 7180 | 0.0007 | - | - | - | - |
| 0.7779 | 7190 | 0.0007 | - | - | - | - |
| 0.7790 | 7200 | 0.0007 | - | - | - | - |
| 0.7800 | 7210 | 0.0006 | - | - | - | - |
| 0.7811 | 7220 | 0.0007 | - | - | - | - |
| 0.7822 | 7230 | 0.0007 | - | - | - | - |
| 0.7833 | 7240 | 0.0006 | - | - | - | - |
| 0.7844 | 7250 | 0.0007 | - | - | - | - |
| 0.7855 | 7260 | 0.0007 | - | - | - | - |
| 0.7865 | 7270 | 0.0006 | - | - | - | - |
| 0.7876 | 7280 | 0.0007 | - | - | - | - |
| 0.7887 | 7290 | 0.0007 | - | - | - | - |
| 0.7898 | 7300 | 0.0006 | - | - | - | - |
| 0.7909 | 7310 | 0.0007 | - | - | - | - |
| 0.7920 | 7320 | 0.0007 | - | - | - | - |
| 0.7930 | 7330 | 0.0007 | - | - | - | - |
| 0.7941 | 7340 | 0.0007 | - | - | - | - |
| 0.7952 | 7350 | 0.0007 | - | - | - | - |
| 0.7963 | 7360 | 0.0006 | - | - | - | - |
| 0.7974 | 7370 | 0.0007 | - | - | - | - |
| 0.7984 | 7380 | 0.0006 | - | - | - | - |
| 0.7995 | 7390 | 0.0007 | - | - | - | - |
| 0.8006 | 7400 | 0.0006 | - | - | - | - |
| 0.8017 | 7410 | 0.0007 | - | - | - | - |
| 0.8028 | 7420 | 0.0007 | - | - | - | - |
| 0.8039 | 7430 | 0.0007 | - | - | - | - |
| 0.8049 | 7440 | 0.0007 | - | - | - | - |
| 0.8060 | 7450 | 0.0006 | - | - | - | - |
| 0.8071 | 7460 | 0.0006 | - | - | - | - |
| 0.8082 | 7470 | 0.0007 | - | - | - | - |
| 0.8093 | 7480 | 0.0007 | - | - | - | - |
| 0.8103 | 7490 | 0.0007 | - | - | - | - |
| 0.8114 | 7500 | 0.0007 | - | - | - | - |
| 0.8125 | 7510 | 0.0007 | - | - | - | - |
| 0.8136 | 7520 | 0.0007 | - | - | - | - |
| 0.8147 | 7530 | 0.0007 | - | - | - | - |
| 0.8158 | 7540 | 0.0007 | - | - | - | - |
| 0.8168 | 7550 | 0.0006 | - | - | - | - |
| 0.8179 | 7560 | 0.0007 | - | - | - | - |
| 0.8190 | 7570 | 0.0006 | - | - | - | - |
| 0.8201 | 7580 | 0.0007 | - | - | - | - |
| 0.8212 | 7590 | 0.0006 | - | - | - | - |
| 0.8222 | 7600 | 0.0007 | - | - | - | - |
| 0.8233 | 7610 | 0.0006 | - | - | - | - |
| 0.8244 | 7620 | 0.0007 | - | - | - | - |
| 0.8255 | 7630 | 0.0007 | - | - | - | - |
| 0.8266 | 7640 | 0.0007 | - | - | - | - |
| 0.8277 | 7650 | 0.0007 | - | - | - | - |
| 0.8287 | 7660 | 0.0007 | - | - | - | - |
| 0.8298 | 7670 | 0.0007 | - | - | - | - |
| 0.8309 | 7680 | 0.0007 | - | - | - | - |
| 0.8320 | 7690 | 0.0007 | - | - | - | - |
| 0.8331 | 7700 | 0.0007 | - | - | - | - |
| 0.8341 | 7710 | 0.0006 | - | - | - | - |
| 0.8352 | 7720 | 0.0007 | - | - | - | - |
| 0.8363 | 7730 | 0.0007 | - | - | - | - |
| 0.8374 | 7740 | 0.0006 | - | - | - | - |
| 0.8385 | 7750 | 0.0007 | - | - | - | - |
| 0.8396 | 7760 | 0.0007 | - | - | - | - |
| 0.8406 | 7770 | 0.0007 | - | - | - | - |
| 0.8417 | 7780 | 0.0007 | - | - | - | - |
| 0.8428 | 7790 | 0.0007 | - | - | - | - |
| 0.8439 | 7800 | 0.0006 | - | - | - | - |
| 0.8450 | 7810 | 0.0007 | - | - | - | - |
| 0.8460 | 7820 | 0.0007 | - | - | - | - |
| 0.8471 | 7830 | 0.0007 | - | - | - | - |
| 0.8482 | 7840 | 0.0007 | - | - | - | - |
| 0.8493 | 7850 | 0.0006 | - | - | - | - |
| 0.8504 | 7860 | 0.0006 | - | - | - | - |
| 0.8515 | 7870 | 0.0007 | - | - | - | - |
| 0.8525 | 7880 | 0.0006 | - | - | - | - |
| 0.8536 | 7890 | 0.0007 | - | - | - | - |
| 0.8547 | 7900 | 0.0006 | - | - | - | - |
| 0.8558 | 7910 | 0.0006 | - | - | - | - |
| 0.8569 | 7920 | 0.0006 | - | - | - | - |
| 0.8579 | 7930 | 0.0006 | - | - | - | - |
| 0.8590 | 7940 | 0.0006 | - | - | - | - |
| 0.8601 | 7950 | 0.0007 | - | - | - | - |
| 0.8612 | 7960 | 0.0007 | - | - | - | - |
| 0.8623 | 7970 | 0.0007 | - | - | - | - |
| 0.8634 | 7980 | 0.0006 | - | - | - | - |
| 0.8644 | 7990 | 0.0007 | - | - | - | - |
| 0.8655 | 8000 | 0.0006 | 0.0006 | 0.4942 | 0.7970 | - |
| 0.8666 | 8010 | 0.0007 | - | - | - | - |
| 0.8677 | 8020 | 0.0007 | - | - | - | - |
| 0.8688 | 8030 | 0.0007 | - | - | - | - |
| 0.8698 | 8040 | 0.0006 | - | - | - | - |
| 0.8709 | 8050 | 0.0007 | - | - | - | - |
| 0.8720 | 8060 | 0.0006 | - | - | - | - |
| 0.8731 | 8070 | 0.0007 | - | - | - | - |
| 0.8742 | 8080 | 0.0007 | - | - | - | - |
| 0.8753 | 8090 | 0.0006 | - | - | - | - |
| 0.8763 | 8100 | 0.0007 | - | - | - | - |
| 0.8774 | 8110 | 0.0006 | - | - | - | - |
| 0.8785 | 8120 | 0.0007 | - | - | - | - |
| 0.8796 | 8130 | 0.0006 | - | - | - | - |
| 0.8807 | 8140 | 0.0006 | - | - | - | - |
| 0.8817 | 8150 | 0.0007 | - | - | - | - |
| 0.8828 | 8160 | 0.0006 | - | - | - | - |
| 0.8839 | 8170 | 0.0007 | - | - | - | - |
| 0.8850 | 8180 | 0.0006 | - | - | - | - |
| 0.8861 | 8190 | 0.0007 | - | - | - | - |
| 0.8872 | 8200 | 0.0006 | - | - | - | - |
| 0.8882 | 8210 | 0.0007 | - | - | - | - |
| 0.8893 | 8220 | 0.0006 | - | - | - | - |
| 0.8904 | 8230 | 0.0007 | - | - | - | - |
| 0.8915 | 8240 | 0.0006 | - | - | - | - |
| 0.8926 | 8250 | 0.0007 | - | - | - | - |
| 0.8936 | 8260 | 0.0007 | - | - | - | - |
| 0.8947 | 8270 | 0.0007 | - | - | - | - |
| 0.8958 | 8280 | 0.0007 | - | - | - | - |
| 0.8969 | 8290 | 0.0007 | - | - | - | - |
| 0.8980 | 8300 | 0.0007 | - | - | - | - |
| 0.8991 | 8310 | 0.0006 | - | - | - | - |
| 0.9001 | 8320 | 0.0007 | - | - | - | - |
| 0.9012 | 8330 | 0.0006 | - | - | - | - |
| 0.9023 | 8340 | 0.0007 | - | - | - | - |
| 0.9034 | 8350 | 0.0006 | - | - | - | - |
| 0.9045 | 8360 | 0.0007 | - | - | - | - |
| 0.9056 | 8370 | 0.0007 | - | - | - | - |
| 0.9066 | 8380 | 0.0007 | - | - | - | - |
| 0.9077 | 8390 | 0.0007 | - | - | - | - |
| 0.9088 | 8400 | 0.0006 | - | - | - | - |
| 0.9099 | 8410 | 0.0006 | - | - | - | - |
| 0.9110 | 8420 | 0.0007 | - | - | - | - |
| 0.9120 | 8430 | 0.0006 | - | - | - | - |
| 0.9131 | 8440 | 0.0007 | - | - | - | - |
| 0.9142 | 8450 | 0.0006 | - | - | - | - |
| 0.9153 | 8460 | 0.0007 | - | - | - | - |
| 0.9164 | 8470 | 0.0006 | - | - | - | - |
| 0.9175 | 8480 | 0.0006 | - | - | - | - |
| 0.9185 | 8490 | 0.0006 | - | - | - | - |
| 0.9196 | 8500 | 0.0007 | - | - | - | - |
| 0.9207 | 8510 | 0.0006 | - | - | - | - |
| 0.9218 | 8520 | 0.0007 | - | - | - | - |
| 0.9229 | 8530 | 0.0007 | - | - | - | - |
| 0.9239 | 8540 | 0.0006 | - | - | - | - |
| 0.9250 | 8550 | 0.0007 | - | - | - | - |
| 0.9261 | 8560 | 0.0006 | - | - | - | - |
| 0.9272 | 8570 | 0.0007 | - | - | - | - |
| 0.9283 | 8580 | 0.0006 | - | - | - | - |
| 0.9294 | 8590 | 0.0006 | - | - | - | - |
| 0.9304 | 8600 | 0.0006 | - | - | - | - |
| 0.9315 | 8610 | 0.0007 | - | - | - | - |
| 0.9326 | 8620 | 0.0007 | - | - | - | - |
| 0.9337 | 8630 | 0.0007 | - | - | - | - |
| 0.9348 | 8640 | 0.0006 | - | - | - | - |
| 0.9358 | 8650 | 0.0006 | - | - | - | - |
| 0.9369 | 8660 | 0.0006 | - | - | - | - |
| 0.9380 | 8670 | 0.0007 | - | - | - | - |
| 0.9391 | 8680 | 0.0007 | - | - | - | - |
| 0.9402 | 8690 | 0.0007 | - | - | - | - |
| 0.9413 | 8700 | 0.0007 | - | - | - | - |
| 0.9423 | 8710 | 0.0006 | - | - | - | - |
| 0.9434 | 8720 | 0.0006 | - | - | - | - |
| 0.9445 | 8730 | 0.0007 | - | - | - | - |
| 0.9456 | 8740 | 0.0006 | - | - | - | - |
| 0.9467 | 8750 | 0.0006 | - | - | - | - |
| 0.9477 | 8760 | 0.0006 | - | - | - | - |
| 0.9488 | 8770 | 0.0006 | - | - | - | - |
| 0.9499 | 8780 | 0.0006 | - | - | - | - |
| 0.9510 | 8790 | 0.0006 | - | - | - | - |
| 0.9521 | 8800 | 0.0006 | - | - | - | - |
| 0.9532 | 8810 | 0.0007 | - | - | - | - |
| 0.9542 | 8820 | 0.0006 | - | - | - | - |
| 0.9553 | 8830 | 0.0006 | - | - | - | - |
| 0.9564 | 8840 | 0.0006 | - | - | - | - |
| 0.9575 | 8850 | 0.0006 | - | - | - | - |
| 0.9586 | 8860 | 0.0007 | - | - | - | - |
| 0.9596 | 8870 | 0.0007 | - | - | - | - |
| 0.9607 | 8880 | 0.0007 | - | - | - | - |
| 0.9618 | 8890 | 0.0007 | - | - | - | - |
| 0.9629 | 8900 | 0.0007 | - | - | - | - |
| 0.9640 | 8910 | 0.0007 | - | - | - | - |
| 0.9651 | 8920 | 0.0007 | - | - | - | - |
| 0.9661 | 8930 | 0.0007 | - | - | - | - |
| 0.9672 | 8940 | 0.0007 | - | - | - | - |
| 0.9683 | 8950 | 0.0006 | - | - | - | - |
| 0.9694 | 8960 | 0.0007 | - | - | - | - |
| 0.9705 | 8970 | 0.0007 | - | - | - | - |
| 0.9715 | 8980 | 0.0006 | - | - | - | - |
| 0.9726 | 8990 | 0.0007 | - | - | - | - |
| 0.9737 | 9000 | 0.0007 | 0.0006 | 0.4973 | 0.7955 | - |
| 0.9748 | 9010 | 0.0006 | - | - | - | - |
| 0.9759 | 9020 | 0.0006 | - | - | - | - |
| 0.9770 | 9030 | 0.0006 | - | - | - | - |
| 0.9780 | 9040 | 0.0007 | - | - | - | - |
| 0.9791 | 9050 | 0.0007 | - | - | - | - |
| 0.9802 | 9060 | 0.0007 | - | - | - | - |
| 0.9813 | 9070 | 0.0007 | - | - | - | - |
| 0.9824 | 9080 | 0.0007 | - | - | - | - |
| 0.9834 | 9090 | 0.0006 | - | - | - | - |
| 0.9845 | 9100 | 0.0007 | - | - | - | - |
| 0.9856 | 9110 | 0.0007 | - | - | - | - |
| 0.9867 | 9120 | 0.0007 | - | - | - | - |
| 0.9878 | 9130 | 0.0007 | - | - | - | - |
| 0.9889 | 9140 | 0.0006 | - | - | - | - |
| 0.9899 | 9150 | 0.0007 | - | - | - | - |
| 0.9910 | 9160 | 0.0007 | - | - | - | - |
| 0.9921 | 9170 | 0.0006 | - | - | - | - |
| 0.9932 | 9180 | 0.0007 | - | - | - | - |
| 0.9943 | 9190 | 0.0007 | - | - | - | - |
| 0.9953 | 9200 | 0.0007 | - | - | - | - |
| 0.9964 | 9210 | 0.0007 | - | - | - | - |
| 0.9975 | 9220 | 0.0007 | - | - | - | - |
| 0.9986 | 9230 | 0.0007 | - | - | - | - |
| 0.9997 | 9240 | 0.0007 | - | - | - | - |
| 1.0 | 9243 | - | - | 0.4962 | - | 0.7185 |