---
base_model: dbourget/pb-ds1-48K
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
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:106810
- loss:CosineSimilarityLoss
widget:
- source_sentence: In The Law of Civilization and Decay, Brooks provides a detailed
look at the rise and fall of civilizations, offering a critical perspective on
the impact of capitalism. As societies become prosperous, their pursuit of wealth
ultimately leads to their own downfall as greed takes over.
sentences:
- Patrick Todd's The Open Future argues that all future contingent statements, such
as 'It will rain tomorrow', are inherently false.
- If propositions are made true in virtue of corresponding to facts, then what are
the truth-makers of true negative propositions such as ‘The apple is not red’?
Russell argued that there must be negative facts to account for what makes true
negative propositions true and false positive propositions false. Others, more
parsimonious in their ontological commitments, have attempted to avoid them. Wittgenstein
rejected them since he was loath to think that the sign for negation referred
to a negative element in a fact. A contemporary of Russell’s, Raphael Demos, attempted
to eliminate them by appealing to ‘incompatibility’ facts. More recently, Armstrong
has appealed to the totality of positive facts as the ground of the truth of true
negative propositions. Oaklander and Miracchi have suggested that the absence
or non-existence of the positive fact (which is not itself a further fact) is
the basis of a positive proposition being false and therefore of the truth of
its negation.
- The Law of Civilization and Decay is an overview of history, articulating Brooks'
critical view of capitalism. A civilization grows wealthy, and then its wealth
causes it to crumble upon itself due to greed.
- source_sentence: It is generally accepted that the development of the modern sciences
is rooted in experiment. Yet for a long time, experimentation did not occupy a
prominent role, neither in philosophy nor in history of science. With the ‘practical
turn’ in studying the sciences and their history, this has begun to change. This
paper is concerned with systems and cultures of experimentation and the consistencies
that are generated within such systems and cultures. The first part of the paper
exposes the forms of historical and structural coherence that characterize the
experimental exploration of epistemic objects. In the second part, a particular
experimental culture in the life sciences is briefly described as an example.
A survey will be given of what it means and what it takes to analyze biological
functions in the test tube
sentences:
- Experimentation has long been overlooked in the study of science, but with a new
focus on practical aspects, this is starting to change. This paper explores the
systems and cultures of experimentation and the patterns that emerge within them.
The first part discusses the historical and structural coherence of experimental
exploration. The second part provides a brief overview of an experimental culture
in the life sciences. The paper concludes with a discussion on analyzing biological
functions in the test tube.
- Hintikka and Mutanen have introduced Trail-And-Error machines as a new way to
think about computation, expanding on the traditional Turing machine model. This
innovation opens up new possibilities in the field of computation theory.
- As Allaire and Firsirotu (1984) pointed out over a decade ago, the concept of
culture seemed to be sliding inexorably into a superficial explanatory pool that
promised everything and nothing. However, since then, some sophisticated and interesting
theoretical developments have prevented drowning in the pool of superficiality
and hence theoretical redundancy. The purpose of this article is to build upon
such theoretical developments and to introduce an approach that maintains that
culture can be theorized in the same way as structure, possessing irreducible
powers and properties that predispose organizational actors towards specific courses
of action. The morphogenetic approach is the methodological complement of transcendental
realism, providing explanatory leverage on the conditions that maintain for cultural
change or stability.
- source_sentence: 'This chapter examines three approaches to applied political and
legal philosophy: Standard activism is primarily addressed to other philosophers,
adopts an indirect and coincidental role in creating change, and counts articulating
sound arguments as success. Extreme activism, in contrast, is a form of applied
philosophy directly addressed to policy-makers, with the goal of bringing about
a particular outcome, and measures success in terms of whether it makes a direct
causal contribution to that goal. Finally, conceptual activism (like standard
activism), primarily targets an audience of fellow philosophers, bears a distant,
non-direct, relation to a desired outcome, and counts success in terms of whether
it encourages a particular understanding and adoption of the concepts under examination.'
sentences:
- John Rawls’ resistance to any kind of global egalitarian principle has seemed
strange and unconvincing to many commentators, including those generally supportive
of Rawls’ project. His rejection of a global egalitarian principle seems to rely
on an assumption that states are economically bounded and separate from one another,
which is not an accurate portrayal of economic relations among states in our globalised
world. In this article, I examine the implications of the domestic theory of justice
as fairness to argue that Rawls has good reason to insist on economically bounded
states. I argue that certain central features of the contemporary global economy,
particularly the free movement of capital across borders, undermine the distributional
autonomy required for states to realise Rawls’ principles of justice, and the
domestic theory thus requires a certain degree of economic separation among states
prior to the convening of the international original position. Given this, I defend
Rawls’ reluctance to endorse a global egalitarian principle and defend a policy
regime of international capital controls, to restore distributional autonomy and
make the realisation of the principles of justice as fairness possible.
- 'Bibliography of the writings by Hilary Putnam: 16 books, 198 articles, 10 translations
into German (up to 1994).'
- The jurisprudence under international human rights treaties has had a considerable
impact across countries. Known for addressing complex agendas, the work of expert
bodies under the treaties has been credited and relied upon for filling the gaps
in the realization of several objectives, including the peace and security agenda. In
1982, the Human Rights Committee (ICCPR), in a General Comment observed that “states
have the supreme duty to prevent wars, acts of genocide and other acts of mass
violence ... Every effort … to avert the danger of war, especially thermonuclear
war, and to strengthen international peace and security would constitute the most
important condition and guarantee for the safeguarding of the right to life.”
Over the years, all treaty bodies have contributed in this direction, endorsing
peace and security so as “to protect people against direct and structural violence
… as systemic problems and not merely as isolated incidents …”. A closer look
at the jurisprudence on peace and security, emanating from treaty monitoring mechanisms
including state periodic reports, interpretive statements, the individual communications
procedure, and others, reveals its distinctive nature
- source_sentence: Autonomist accounts of cognitive science suggest that cognitive
model building and theory construction (can or should) proceed independently of
findings in neuroscience. Common functionalist justifications of autonomy rely
on there being relatively few constraints between neural structure and cognitive
function (e.g., Weiskopf, 2011). In contrast, an integrative mechanistic perspective
stresses the mutual constraining of structure and function (e.g., Piccinini &
Craver, 2011; Povich, 2015). In this paper, I show how model-based cognitive neuroscience
(MBCN) epitomizes the integrative mechanistic perspective and concentrates the
most revolutionary elements of the cognitive neuroscience revolution (Boone &
Piccinini, 2016). I also show how the prominent subset account of functional realization
supports the integrative mechanistic perspective I take on MBCN and use it to
clarify the intralevel and interlevel components of integration.
sentences:
- Fictional truth, or truth in fiction/pretense, has been the object of extended
scrutiny among philosophers and logicians in recent decades. Comparatively little
attention, however, has been paid to its inferential relationships with time and
with certain deliberate and contingent human activities, namely, the creation
of fictional works. The aim of the paper is to contribute to filling the gap.
Toward this goal, a formal framework is outlined that is consistent with a variety
of conceptions of fictional truth and based upon a specific formal treatment of
time and agency, that of so-called stit logics. Moreover, a complete axiomatic
theory of fiction-making TFM is defined, where fiction-making is understood as
the exercise of agency and choice in time over what is fictionally true. The language
\ of TFM is an extension of the language of propositional logic, with the addition
of temporal and modal operators. A distinctive feature of \ with respect to other
modal languages is a variety of operators having to do with fictional truth, including
a ‘fictionality’ operator \ . Some applications of TFM are outlined, and some
interesting linguistic and inferential phenomena, which are not so easily dealt
with in other frameworks, are accounted for
- 'We have structured our response according to five questions arising from the
commentaries: (i) What is sentience? (ii) Is sentience a necessary or sufficient
condition for moral standing? (iii) What methods should guide comparative cognitive
research in general, and specifically in studying invertebrates? (iv) How should
we balance scientific uncertainty and moral risk? (v) What practical strategies
can help reduce biases and morally dismissive attitudes toward invertebrates?'
- 'In 2007, ten world-renowned neuroscientists proposed “A Decade of the Mind Initiative.”
The contention was that, despite the successes of the Decade of the Brain, “a
fundamental understanding of how the brain gives rise to the mind [was] still
lacking” (2007, 1321). The primary aims of the decade of the mind were “to build
on the progress of the recent Decade of the Brain (1990-99)” by focusing on “four
broad but intertwined areas” of research, including: healing and protecting, understanding,
enriching, and modeling the mind. These four aims were to be the result of “transdisciplinary
and multiagency” research spanning “across disparate fields, such as cognitive
science, medicine, neuroscience, psychology, mathematics, engineering, and computer
science.” The proposal for a decade of the mind prompted many questions (See Spitzer
2008). In this chapter, I address three of them: (1) How do proponents of this
new decade conceive of the mind? (2) Why should a decade be devoted to understanding
it? (3) What should this decade look like?'
- source_sentence: This essay explores the historical and modern perspectives on the
Gettier problem, highlighting the connections between this issue, skepticism,
and relevance. Through methods such as historical analysis, induction, and deduction,
it is found that while contextual theories and varying definitions of knowledge
do not fully address skeptical challenges, they can help clarify our understanding
of knowledge. Ultimately, embracing subjectivity and intuition can provide insight
into what it truly means to claim knowledge.
sentences:
- In this article I present and analyze three popular moral justifications for hunting.
My purpose is to expose the moral terrain of this issue and facilitate more fruitful,
philosophically relevant discussions about the ethics of hunting.
- Teaching competency in bioethics has been a concern since the field's inception.
The first report on the teaching of contemporary bioethics was published in 1976
by The Hastings Center, which concluded that graduate programs were not necessary
at the time. However, the report speculated that future developments may require
new academic structures for graduate education in bioethics. The creation of a
terminal degree in bioethics has its critics, with scholars debating whether bioethics
is a discipline with its own methods and theoretical grounding, a multidisciplinary
field, or something else entirely. Despite these debates, new bioethics training
programs have emerged at all postsecondary levels in the U.S. This essay examines
the number and types of programs and degrees in this growing field.
- 'Objective: In this essay, I will try to track some historical and modern stages
of the discussion on the Gettier problem, and point out the interrelations of
the questions that this problem raises for epistemologists, with sceptical arguments,
and a so-called problem of relevance. Methods: historical analysis, induction,
generalization, deduction, discourse, intuition results: Albeit the contextual
theories of knowledge, the use of different definitions of knowledge, and the
different ways of the uses of knowledge do not resolve all the issues that the
sceptic can put forward, but they can be productive in giving clarity to a concept
of knowledge for us. On the other hand, our knowledge will always have an element
of intuition and subjectivity, however not equating to epistemic luck and probability. Significance
novelty: the approach to the context in general, not giving up being a Subject
may give us a clarity about the sense of what it means to say – “I know”.'
model-index:
- name: SentenceTransformer based on dbourget/pb-ds1-48K
results:
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts dev
type: sts-dev
metrics:
- type: pearson_cosine
value: 0.9378177365442741
name: Pearson Cosine
- type: spearman_cosine
value: 0.8943299298202461
name: Spearman Cosine
- type: pearson_manhattan
value: 0.9709949018414847
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.8969442622028955
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.9711044669329696
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.8966133108746955
name: Spearman Euclidean
- type: pearson_dot
value: 0.9419649751470724
name: Pearson Dot
- type: spearman_dot
value: 0.8551487313582053
name: Spearman Dot
- type: pearson_max
value: 0.9711044669329696
name: Pearson Max
- type: spearman_max
value: 0.8969442622028955
name: Spearman Max
---
# SentenceTransformer based on dbourget/pb-ds1-48K
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [dbourget/pb-ds1-48K](https://huggingface.co/dbourget/pb-ds1-48K). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [dbourget/pb-ds1-48K](https://huggingface.co/dbourget/pb-ds1-48K)
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 tokens
- **Similarity Function:** Cosine Similarity
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("dbourget/pb-ds1-48K-philsim")
# Run inference
sentences = [
'This essay explores the historical and modern perspectives on the Gettier problem, highlighting the connections between this issue, skepticism, and relevance. Through methods such as historical analysis, induction, and deduction, it is found that while contextual theories and varying definitions of knowledge do not fully address skeptical challenges, they can help clarify our understanding of knowledge. Ultimately, embracing subjectivity and intuition can provide insight into what it truly means to claim knowledge.',
'Objective: In this essay, I will try to track some historical and modern stages of the discussion on the Gettier problem, and point out the interrelations of the questions that this problem raises for epistemologists, with sceptical arguments, and a so-called problem of relevance. Methods: historical analysis, induction, generalization, deduction, discourse, intuition results: Albeit the contextual theories of knowledge, the use of different definitions of knowledge, and the different ways of the uses of knowledge do not resolve all the issues that the sceptic can put forward, but they can be productive in giving clarity to a concept of knowledge for us. On the other hand, our knowledge will always have an element of intuition and subjectivity, however not equating to epistemic luck and probability. Significance novelty: the approach to the context in general, not giving up being a Subject may give us a clarity about the sense of what it means to say – “I know”.',
"Teaching competency in bioethics has been a concern since the field's inception. The first report on the teaching of contemporary bioethics was published in 1976 by The Hastings Center, which concluded that graduate programs were not necessary at the time. However, the report speculated that future developments may require new academic structures for graduate education in bioethics. The creation of a terminal degree in bioethics has its critics, with scholars debating whether bioethics is a discipline with its own methods and theoretical grounding, a multidisciplinary field, or something else entirely. Despite these debates, new bioethics training programs have emerged at all postsecondary levels in the U.S. This essay examines the number and types of programs and degrees in this growing field.",
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
## Evaluation
### Metrics
#### 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 | 0.9378 |
| **spearman_cosine** | **0.8943** |
| pearson_manhattan | 0.971 |
| spearman_manhattan | 0.8969 |
| pearson_euclidean | 0.9711 |
| spearman_euclidean | 0.8966 |
| pearson_dot | 0.942 |
| spearman_dot | 0.8551 |
| pearson_max | 0.9711 |
| spearman_max | 0.8969 |
## Training Details
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 190
- `per_device_eval_batch_size`: 190
- `learning_rate`: 5e-06
- `num_train_epochs`: 2
- `warmup_ratio`: 0.1
- `bf16`: True
- `batch_sampler`: no_duplicates
#### All Hyperparameters
Click to expand
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 190
- `per_device_eval_batch_size`: 190
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `learning_rate`: 5e-06
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 2
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: True
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: False
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
Click to expand
| Epoch | Step | Training Loss | loss | sts-dev_spearman_cosine |
|:------:|:----:|:-------------:|:------:|:-----------------------:|
| 0 | 0 | - | - | 0.8229 |
| 0.0178 | 10 | 0.0545 | - | - |
| 0.0355 | 20 | 0.0556 | - | - |
| 0.0533 | 30 | 0.0502 | - | - |
| 0.0710 | 40 | 0.0497 | - | - |
| 0.0888 | 50 | 0.0413 | - | - |
| 0.1066 | 60 | 0.0334 | - | - |
| 0.1243 | 70 | 0.0238 | - | - |
| 0.1421 | 80 | 0.0206 | - | - |
| 0.1599 | 90 | 0.0167 | - | - |
| 0.1776 | 100 | 0.0146 | 0.0725 | 0.8788 |
| 0.1954 | 110 | 0.0127 | - | - |
| 0.2131 | 120 | 0.0125 | - | - |
| 0.2309 | 130 | 0.0115 | - | - |
| 0.2487 | 140 | 0.0116 | - | - |
| 0.2664 | 150 | 0.0111 | - | - |
| 0.2842 | 160 | 0.0107 | - | - |
| 0.3020 | 170 | 0.0113 | - | - |
| 0.3197 | 180 | 0.0106 | - | - |
| 0.3375 | 190 | 0.0099 | - | - |
| 0.3552 | 200 | 0.0092 | 0.0207 | 0.8856 |
| 0.3730 | 210 | 0.0097 | - | - |
| 0.3908 | 220 | 0.0099 | - | - |
| 0.4085 | 230 | 0.0087 | - | - |
| 0.4263 | 240 | 0.0087 | - | - |
| 0.4440 | 250 | 0.0082 | - | - |
| 0.4618 | 260 | 0.0083 | - | - |
| 0.4796 | 270 | 0.0089 | - | - |
| 0.4973 | 280 | 0.0082 | - | - |
| 0.5151 | 290 | 0.0078 | - | - |
| 0.5329 | 300 | 0.0081 | 0.0078 | 0.8891 |
| 0.5506 | 310 | 0.0081 | - | - |
| 0.5684 | 320 | 0.0072 | - | - |
| 0.5861 | 330 | 0.0084 | - | - |
| 0.6039 | 340 | 0.0083 | - | - |
| 0.6217 | 350 | 0.0078 | - | - |
| 0.6394 | 360 | 0.0077 | - | - |
| 0.6572 | 370 | 0.008 | - | - |
| 0.6750 | 380 | 0.0073 | - | - |
| 0.6927 | 390 | 0.008 | - | - |
| 0.7105 | 400 | 0.0073 | 0.0058 | 0.8890 |
| 0.7282 | 410 | 0.0075 | - | - |
| 0.7460 | 420 | 0.0077 | - | - |
| 0.7638 | 430 | 0.0074 | - | - |
| 0.7815 | 440 | 0.0073 | - | - |
| 0.7993 | 450 | 0.007 | - | - |
| 0.8171 | 460 | 0.0043 | - | - |
| 0.8348 | 470 | 0.0052 | - | - |
| 0.8526 | 480 | 0.0046 | - | - |
| 0.8703 | 490 | 0.0073 | - | - |
| 0.8881 | 500 | 0.0056 | 0.0069 | 0.8922 |
| 0.9059 | 510 | 0.0059 | - | - |
| 0.9236 | 520 | 0.0045 | - | - |
| 0.9414 | 530 | 0.0033 | - | - |
| 0.9591 | 540 | 0.0058 | - | - |
| 0.9769 | 550 | 0.0056 | - | - |
| 0.9947 | 560 | 0.0046 | - | - |
| 1.0124 | 570 | 0.003 | - | - |
| 1.0302 | 580 | 0.0039 | - | - |
| 1.0480 | 590 | 0.0032 | - | - |
| 1.0657 | 600 | 0.0031 | 0.0029 | 0.8931 |
| 1.0835 | 610 | 0.0046 | - | - |
| 1.1012 | 620 | 0.003 | - | - |
| 1.1190 | 630 | 0.0021 | - | - |
| 1.1368 | 640 | 0.0031 | - | - |
| 1.1545 | 650 | 0.0035 | - | - |
| 1.1723 | 660 | 0.0033 | - | - |
| 1.1901 | 670 | 0.0024 | - | - |
| 1.2078 | 680 | 0.0012 | - | - |
| 1.2256 | 690 | 0.0075 | - | - |
| 1.2433 | 700 | 0.0028 | 0.0036 | 0.8945 |
| 1.2611 | 710 | 0.0033 | - | - |
| 1.2789 | 720 | 0.0023 | - | - |
| 1.2966 | 730 | 0.0034 | - | - |
| 1.3144 | 740 | 0.0018 | - | - |
| 1.3321 | 750 | 0.0016 | - | - |
| 1.3499 | 760 | 0.0025 | - | - |
| 1.3677 | 770 | 0.002 | - | - |
| 1.3854 | 780 | 0.0016 | - | - |
| 1.4032 | 790 | 0.0018 | - | - |
| 1.4210 | 800 | 0.003 | 0.0027 | 0.8944 |
| 1.4387 | 810 | 0.0018 | - | - |
| 1.4565 | 820 | 0.0008 | - | - |
| 1.4742 | 830 | 0.0014 | - | - |
| 1.4920 | 840 | 0.0025 | - | - |
| 1.5098 | 850 | 0.0026 | - | - |
| 1.5275 | 860 | 0.0012 | - | - |
| 1.5453 | 870 | 0.001 | - | - |
| 1.5631 | 880 | 0.001 | - | - |
| 1.5808 | 890 | 0.0012 | - | - |
| 1.5986 | 900 | 0.0021 | 0.0021 | 0.8952 |
| 1.6163 | 910 | 0.0016 | - | - |
| 1.6341 | 920 | 0.0008 | - | - |
| 1.6519 | 930 | 0.0008 | - | - |
| 1.6696 | 940 | 0.0009 | - | - |
| 1.6874 | 950 | 0.0004 | - | - |
| 1.7052 | 960 | 0.0003 | - | - |
| 1.7229 | 970 | 0.0007 | - | - |
| 1.7407 | 980 | 0.0007 | - | - |
| 1.7584 | 990 | 0.0011 | - | - |
| 1.7762 | 1000 | 0.0007 | 0.0029 | 0.8952 |
| 1.7940 | 1010 | 0.0008 | - | - |
| 1.8117 | 1020 | 0.001 | - | - |
| 1.8295 | 1030 | 0.0006 | - | - |
| 1.8472 | 1040 | 0.0006 | - | - |
| 1.8650 | 1050 | 0.0015 | - | - |
| 1.8828 | 1060 | 0.0009 | - | - |
| 1.9005 | 1070 | 0.0005 | - | - |
| 1.9183 | 1080 | 0.0006 | - | - |
| 1.9361 | 1090 | 0.0021 | - | - |
| 1.9538 | 1100 | 0.0009 | 0.0023 | 0.8943 |
| 1.9716 | 1110 | 0.0007 | - | - |
| 1.9893 | 1120 | 0.0003 | - | - |