高校英文法と機械学習例文

📌はじめに

生成AIを使って「わからない部分」を聞いてみよう

📌【他動詞っぽい「自動詞」】一覧と例文（前置詞付き）

自動詞	意味	用法	機械学習例文（ML文脈）
look	見る	look at	The analyst looked at the loss curve during training.
listen	聞く	listen to	Engineers listened to feedback from the model users.
glance	ちらっと見る	glance at	He glanced at the ROC curve before moving to the next experiment.
stare	じっと見る	stare at	She stared at the anomaly in the scatter plot.
wait	待つ	wait for	The script is waiting for the GPU to initialize.
participate	参加する	participate in	Many students participated in the Kaggle competition.
refrain	控える	refrain from	Please refrain from changing the model architecture during the test phase.
compete	競争する	compete with	This algorithm can now compete with state-of-the-art methods.
major	専攻する	major in	He majored in machine learning and natural language processing.
deal	扱う	deal with	We must deal with imbalanced data in binary classification.
cope	対処する	cope with	The optimizer copes with vanishing gradients in deep networks.
think	考える	think of/about	We are thinking about applying reinforcement learning to robotics.
hope	望む	hope for	We hope for a lower error rate in the validation set.
long	切望する	long for	Researchers long for a breakthrough in explainable AI.
dream	夢見る	dream of/about	Many engineers dream of building general AI.
complain	不平を言う	complain about	Some users complained about the model's bias.
insist	主張する	insist on	The author insists on using a probabilistic framework.
decide	決定する	decide on	The team decided on using the Adam optimizer instead of SGD.

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📌【自動詞っぽい「他動詞」】一覧と例文（目的語を直にとる）

他動詞	意味	用法例	機械学習例文（ML文脈）
visit	を訪れる	visit a dataset	We visited the UCI repository to download datasets.
discuss	について議論する	discuss the algorithm	The paper discusses the drawbacks of the naive Bayes classifier.
enter	に入る	enter the data pipeline	The input enters the preprocessing pipeline.
marry	と結婚する	marry the two methods	We marry CNNs with RNNs in a hybrid architecture.
leave	を去る	leave the training phase	The model left the training phase and entered evaluation.
reach	に到着する	reach convergence	The training process reached convergence after 20 epochs.
approach	に近づく	approach optimal performance	The new method approaches optimal performance on large datasets.
mention	に言及する	mention the issue	The authors mention overfitting in Section 3.
inhabit	に住む	inhabit the domain space	Outliers inhabit a very different region of the feature space.
answer	に答える	answer the question	The model answers the user’s question using a large language model.
face	に直面する	face a challenge	The model faces the challenge of real-time prediction.
consider	を考慮する	consider all features	Consider all features before applying dimensionality reduction.
follow	に従う	follow the protocol	Please follow the preprocessing protocol strictly.
resemble	に似ている	resemble the training data	The test data must resemble the training data to ensure generalization.
match	に合う	match the input shape	This tensor doesn’t match the input shape expected by the model.
suit	に似合う	suit the task	This model suits image classification better than regression.
greet	に挨拶する	greet the participants	The host greeted the participants at the AI workshop.

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📌【意味が大きく変わる動詞】例文（自動詞と他動詞の意味の差異を明示）

動詞	自動詞の意味	他動詞の意味	機械学習例文（ML文脈）
stand	立つ	を我慢する	The AI system cannot stand ambiguous input.
run	走る	を運営する	He runs a startup that builds large-scale ML systems.
do	十分である（自動詞）	を行う	This data augmentation does the job well.
succeed	成功する	の後を継ぐ	GPT-4 succeeded GPT-3 with better language capabilities.
yield	屈する	をもたらす	This approach yields better F1 scores than previous ones.
attend	注意を払う	に出席する	The professor attended the conference on Bayesian inference.
associate	付き合う、関係する	を関連づける	People often associate overfitting with high model complexity.

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📌 自動詞と他動詞で語形が異なる動詞ペア 一覧（英語学習＋ML文脈）

日本語	自動詞	他動詞	自動詞例文（ML文脈）	他動詞例文（ML文脈）
横たわる／横たえる	lie – lay – lain – lying	lay – laid – laid – laying	After debugging, the engineer lay down on the office couch.	The developer laid the sensor module on the circuit board.
上がる／上げる	rise – rose – risen – rising	raise – raised – raised – raising	GPU usage rose sharply during model training.	They raised the learning rate to speed up convergence.
座る／座らせる	sit – sat – sat – sitting	seat – seated – seated – seating	She sat in front of the terminal to review the training logs.	The system automatically seated the user in a virtual room.
起きる／起こす	rise（同上）	raise（同上）	An unexpected error rose during batch processing.	The script raised an exception in line 52.
倒れる／倒す	fall – fell – fallen – falling	drop – dropped – dropped – dropping	The accuracy suddenly fell after epoch 20.	He dropped the model from the pipeline due to poor performance.
集まる／集める	gather（群れる）	collect（集める）	Developers gathered around the whiteboard to discuss the architecture.	We collected over 10,000 annotated images for training.
始まる／始める	begin – began – begun – beginning	start – started – started – starting	The conference begins at 9 a.m.	We started training the model with new hyperparameters.
変わる／変える	change（自・他で共有）	modify（他動詞のみ）	The system automatically changed due to the new update.	The engineer modified the activation function from ReLU to GELU.
到着する／到着させる	arrive（自動詞）	bring（連れてくる）	The data arrived at the server at 3 a.m.	The script brought all necessary files into the workspace.

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📘 前置詞 to + Ving をとる表現一覧

No	表現	意味	機械学習・ビジネス文脈の例文
1	look forward to Ving	Vするのを楽しみに待つ	We look forward to deploying the model next month.
2a	be used [accustomed] to Ving	Vするのに慣れている	The team is used to tuning hyperparameters manually.
2b	get used [accustomed] to Ving	Vするのに慣れる	You'll soon get used to working with large datasets.
3	object to Ving	Vすることに反対する	Some users object to sharing personal data with the model.
4	What do you say to Ving?	Vしませんか	What do you say to trying ensemble learning?
5	when it comes to Ving	Vすることになると	When it comes to labeling data, quality is crucial.
6	with a view to Ving	Vする目的で（= for the purpose of）	We collected more samples with a view to improving accuracy.
7	devote oneself to Ving	Vするのに没頭する	She devoted herself to developing ethical AI systems.

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📘 名詞節と副詞節の両方を導く接続詞一覧

No	接続詞	副詞節の意味（条件や時間）	名詞節の意味（that節相当）
1	when SV	SがVするとき（時）	いつSがVするか（疑問詞的）
2	if SV	もしSがVするならば（条件）	SがVするかどうか（yes/no）
3	whether SV or not	SがVしようとしまいと（譲歩）	SがVするかどうか（選択）

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✏️ 例文（機械学習・テクノロジー文脈）

1. when SV

• 副詞節:
  When the model finishes training, the evaluation will start.
  → モデルの訓練が終了したとき、評価が始まる。

• 名詞節:
  I don't know when the server will respond.
  → サーバーがいつ応答するか分からない。

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2. if SV

• 副詞節:
  If you tune the parameters carefully, performance will improve.
  → パラメータを丁寧に調整すれば、性能が向上する。

• 名詞節:
  We need to check if the dataset has missing values.
  → データセットに欠損値があるかどうか確認する必要がある。

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3. whether SV or not

• 副詞節:
  We'll proceed whether the results are promising or not.
  → 結果が有望であろうとなかろうと、我々は前に進む。

• 名詞節:
  It’s unclear whether the model generalizes well or not.
  → そのモデルがうまく汎化するかどうかは不明である。

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📘 結果を表す不定詞 まとめ + 英語例文（技術文脈）

No	表現	日本語の意味	機械学習・ビジネス例文（英語）
1	grow up to be ~	成長して〜になる	She grew up to be a leading researcher in AI ethics.
2	live to be + 年齢	〜歳まで生きる	Alan Turing lived to be only 41, but his legacy continues.
3	awake to find ~ = wake up to find ~	目が覚めると〜と気づく	The team awoke to find that the entire dataset had been corrupted overnight.
4	..., only to V	...だが、結局Vしただけだった	He spent months training the model, only to overfit in the final test.
5	..., never to V	...し、そして二度とVしなかった	The system failed during deployment, never to recover.

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📘 「〜と関係がある／ない」という表現一覧

No	表現	日本語訳	機械学習・ビジネス英語文例
1	have nothing to do with ~	～とまったく関係がない	This error has nothing to do with the model architecture.
2	have a lot to do with ~	～と大いに関係がある	Model performance has a lot to do with data quality.
3	have a little to do with ~	～と少し関係がある	The outliers may have a little to do with noise in sensor data.
4	have little to do with ~	～とほとんど関係がない	The dropout rate has little to do with the optimizer choice in this case.

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✅ 使用の注意点：

• a little と little の違い：

  • a little = 肯定的ニュアンス（少しはある）
  • little = 否定的ニュアンス（ほとんどない）

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📘 不可算名詞ベスト10（数えられない名詞）

No	不可算名詞	意味	ML・ビジネス英語例文
1	information	情報	The model was trained on user information.
2	furniture	家具	The new office came with all necessary furniture.
3	money	お金	We don’t have enough money to deploy more GPUs.
4	advice	助言	The mentor gave us useful advice on algorithm design.
5	news	ニュース	There is exciting news about the latest AI breakthrough.
6	homework	宿題	Your machine learning homework is due tomorrow.
7	baggage / luggage	手荷物	The researchers arrived with heavy luggage filled with devices.
8	poetry	詩歌	The AI attempted to generate poetry in the style of Emily Dickinson.
9	scenery	風景	The drone captured beautiful scenery during the data collection.
10	clothing	衣類	The company developed smart clothing with biometric sensors.

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🔄 紛らわしい可算 / 不可算 名詞（意味は似ている）

可算名詞	不可算名詞	意味	ML・技術英語例文
coin	money	硬貨 / お金	This coin has no value, but the money in the wallet does.
bill / notes	money	紙幣 / お金	The notes were counterfeit; real money is needed to purchase servers.
chair / table	furniture	椅子 / 家具	Each desk is separate, but furniture as a whole was preinstalled.
suitcase / bag	baggage / luggage	カバン / 手荷物	He brought three bags, but all his luggage was checked in.
clothes / shoes	clothing	衣服 / 衣類	Clothes are scattered, but the clothing style is consistent.
assignments	homework	宿題（個別）/ 宿題（全体）	The homework includes three assignments due next week.

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📘 1. まぎらわしい動詞（borrow / rent / take / bring など）

動詞	ML文脈での例文
borrow	We borrowed a pre-trained model from the open-source community.
rent	Our team rented extra cloud GPUs to accelerate training.
use	You can use the pretrained weights for fine-tuning.
take	I’ll take the dataset to the external storage server.
bring	Could you bring the model checkpoint to the meeting room?

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📘 2. first を含む表現

表現	ML文脈での例文
first of all	First of all, we need to clean the training data.
for the first time	I deployed a transformer model for the first time yesterday.
at first	At first, the accuracy fluctuated a lot during early epochs.

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📘 3. 「決して〜ではない」を表す表現

表現	ML文脈での例文
far from ~	The model is far from being production-ready.
anything but ~	The debugging process was anything but simple.
not ~ at all	The model isn’t interpretable at all.
by no means ~	This architecture is by no means optimal.
not in the least	I’m not in the least concerned about overfitting here.
not ~ whatever	There’s no performance gain whatever after tuning.
in no way ~	In no way should we deploy this model without testing.

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📘 4. 同格の that を従える名詞

名詞	ML文脈での例文
fact	The fact that the model overfits suggests data leakage.
news	The news that OpenAI released a new model shocked the team.
chance	There's a chance that the loss will diverge.
belief	The belief that bigger models always perform better is misleading.
idea	The idea that AI can reason like humans is still under debate.
opinion	In my opinion that model selection is more important than architecture.
hope	We have hope that the new data augmentation will improve robustness.
rumor	There’s a rumor that the benchmark was leaked online.

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📘 5. 動名詞（Ving）を目的語にとる動詞（句）

動詞（句）	ML文脈での例文
mind Ving	Do you mind retraining the model overnight?
enjoy Ving	I enjoy tuning hyperparameters manually.
give up Ving	We gave up using that optimizer after several failed runs.
avoid Ving	We avoided using large batch sizes to reduce overfitting.
finish Ving	I just finished labeling all the images.
escape being Vpp	The model escaped being penalized by the regularizer.
postpone Ving	We postponed running the final evaluation due to a bug.
stop Ving	He stopped training after the validation loss plateaued.
deny Ving	The developer denied modifying the source code.
admit Ving	She admitted forgetting to normalize the input.
practice Ving	We practiced tuning under different noise conditions.
advise Ving	I advise trying a simpler model first.
miss Ving	We missed capturing the edge cases in the dataset.
suggest Ving	I suggest logging all experiments using MLflow.
consider Ving	We’re considering switching to PyTorch Lightning.

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📘 1. tell / talk / speak / say の使い分け（命令・報告・会話）

動詞	用法説明	ML文脈での例文
tell	tell 人 to V / that SV	The lead told the intern to restart the training job.
talk	talk to 人 / talk about ~	We talked to the DevOps team about deployment latency.
speak	speak to 人 / speak about	He spoke about data augmentation in his presentation.
say	say (to 人) that SV	She said that the loss function needs to be modified.

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📘 2. 不定詞 vs 動名詞（意味の違いに注意）

動詞句	意味	ML文脈例文
remember to V	これから〜することを覚えている	Remember to log the accuracy after each epoch.
remember Ving	過去に〜したのを覚えている	I remember updating the config file.
forget to V	〜するのを忘れる	Don’t forget to save the final model.
forget Ving	〜したのを忘れる	I forgot adjusting the learning rate yesterday.
try to V	〜しようと努力する	We tried to improve inference speed.
try Ving	試しに〜してみる	Try using batch normalization before the activation.
regret to V	残念ながら〜する（未来）	We regret to announce a delay in model release.
regret Ving	〜したのを後悔する	I regret not testing the validation set thoroughly.

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📘 3. 数・量の比較表現

表現	意味	ML文脈例文
no more than ~	たった〜しかない	The model has no more than 2 million parameters.
no less than ~	～もある	We used no less than 50,000 images for training.
no fewer than +可算名詞	～もある	The dataset contains no fewer than 10,000 labels.
not more than ~	多くても	We expect not more than 3 hours for training.
not less than ~	少なくとも	You need not less than 8GB of VRAM to run this model.

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📘 4. 独立不定詞の表現（to不定詞が副詞句のように働く）

表現	意味／使い方	ML文脈例文
to begin with	まず第一に	To begin with, let's prepare the environment.
to tell the truth	実を言うと	To tell the truth, I forgot to back up the model.
so to speak	いわば	This function is, so to speak, the brain of the pipeline.
strange to say	奇妙な話だが	Strange to say, the training loss decreased too quickly.
to make matters worse	さらに悪いことに	To make matters worse, the validation data was corrupted.
needless to say	言うまでもなく	Needless to say, data quality affects performance.
to say nothing of ~	～は言うまでもなく	The code is slow, to say nothing of the memory usage.
to be frank with you	率直に言って	To be frank with you, the model architecture is outdated.
to be sure	確かに	To be sure, the results look promising, but we need tests.
not to say ~	～とは言わないまでも	It's risky, not to say completely wrong.
to say the least	控えめに言っても	The results are impressive, to say the least.
to do + 人 + justice	公正に評価すれば	This summary doesn’t do the algorithm justice.

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📘1. 他動詞＋O＋to不定詞パターン

表現	意味	ML文脈例文
allow O to V	OがVするのを許す	The system allows users to download the model.
advise O to V	OがVするように助言する	We advised the team to validate the dataset first.
ask O to V	OがVするように頼む	They asked the intern to rerun the training.
cause O to V	OにVさせる	The bug caused the server to crash.
enable O to V	OがVするのを可能にする	This optimization enables the model to train faster.
encourage O to V	OがVするように励ます	The results encouraged us to continue tuning.
force O to V	OにVさせる	We had to force the model to stop due to overfitting.
tell O to V	OにVするように言う、命令する	I told the script to resume from checkpoint.

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📘2. 助動詞＋have＋過去分詞（過去の推量・後悔）

表現	意味	ML文脈例文
must have Vpp	〜したに違いない	The optimizer must have converged by now.
should [ought to] have Vpp	～すべきだった	We should have saved the logs.
may have Vpp	～したかもしれない	The update may have introduced a bug.
might have Vpp	～したかもしれない	The model might have overfit the dev set.
cannot have Vpp	～したはずがない	The validation loss cannot have decreased that quickly.

過去の行為を悔やむ表現

表現	意味	ML文脈例文
need not have Vpp	～する必要はなかった	We need not have retrained the entire model.
should [ought to] have Vpp	～すべきだった	You should have double-checked the labels.
had better have Vpp	～したほうがよかった	You had better have backed up the config.

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📘3. to不定詞をとる自動詞・他動詞

自動詞＋to不定詞

表現	意味	ML文脈例文
happen to V	たまたま〜する	I happened to find a better loss function.
prove (to be) ~	～だとわかる	The model proved to be robust to noise.
turn out (to be) ~	～だと判明する	The result turned out to be a data leakage.
come/get to V	Vするようになる	The system got to outperform human baselines.

他動詞＋to不定詞

表現	意味	ML文脈例文
manage to V	なんとかVする	We managed to reduce training time.
hope to V	～することを希望する	I hope to publish the paper soon.
refuse to V	～するのを拒む	The model refused to converge.
offer to V	～することを申し出る	He offered to label the data manually.
decide to V	～することを決める	They decided to use PyTorch instead.
expect to V	～すると思う／予想する	We expect to reach 95% accuracy.
intend to V	～するつもりである	I intend to test the new architecture.
mean to V	～するつもりである	I didn’t mean to stop the training.

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📘4. 分詞構文を用いた慣用表現（～ing）

表現	意味	ML文脈例文
strictly speaking	厳密に言えば	Strictly speaking, the model is a semi-supervised learner.
frankly speaking	率直に言えば	Frankly speaking, the results are underwhelming.
generally speaking	一般的に言えば	Generally speaking, CNNs perform well on image tasks.
roughly speaking	おおざっぱに言えば	Roughly speaking, the accuracy is around 85%.
speaking of ~	～といえば	Speaking of overfitting, have you tried dropout?
judging from ~	～から判断すると	Judging from the logs, the model has converged.
including ~	～を含めて	Including augmented data improved performance.
considering ~	～を考慮すると	Considering the limited data, the result is good.
compared with ~	～と比べると	Compared with baseline models, ours is faster.
given ~	～を考慮すると	Given the time constraints, this is sufficient.

独立分詞構文

表現	意味	ML文脈例文
weather permitting	天候がよければ	Weather permitting, we’ll test the drone model outside.
all things considered	すべてを考慮すれば	All things considered, the results are acceptable.
such being the case	そういう状況であれば	Such being the case, we will retrain the model.
(all) other things being equal	他の条件が同じなら	Other things being equal, larger models generalize better.

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📘5. 比較級・最上級・代名詞 those

最上級・比較級の文例（富士山・時間をベースにした例）

表現	ML文脈例文
X is the most accurate model.	GPT-4 is the most accurate model we've tested.
X is better than any other Y.	This optimizer is faster than any other tested so far.
No other model is better than X.	No other model is better than GPT-4 in text generation.
No model is as fast as X.	No model is as fast as this distilled version.

名詞の反復を避ける代名詞

名詞の種類	代名詞	ML文脈例文
a + 名詞	one	I prefer this one over the older model.
可算名詞の複数形	ones	I tested both models and chose the newer ones.
the + 名詞	the one	The model we deployed is the one trained last week.
the + 名詞の複数形	those	The results of Experiment A are more reliable than those of B.

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📘1. 「ときどき」を表す副詞表現（頻度）

表現	意味	ML文脈例文
from time to time	ときどき	We update the dataset from time to time.
(every) now and then	ときどき	I retrain the model now and then.
now and again	ときどき	The server crashes now and again.
at times	ときどき	At times, the validation accuracy drops suddenly.
occasionally	ときおり	The optimizer occasionally diverges.
sometimes	ときどき	Sometimes the model fails to converge.
once in a while	ごくたまに	We test the backup model once in a while.

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📘2. 比較表現：to を使った「よりも〜」

表現	意味	ML文脈例文
be superior to ~	～より優れている	Transformer is superior to RNN in language tasks.
be inferior to ~	～より劣っている	This lightweight model is inferior to the full version in accuracy.
be senior to ~	～より年上	The senior engineer is more experienced in model tuning.
be junior to ~	～より年下	The intern is junior to the project lead.
prefer A to B	BよりAを好む	We prefer PyTorch to TensorFlow for its flexibility.
be preferable to ~	～より好ましい	A smaller model is preferable to a complex one in production.

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📘3. 過去を示す語句（明確に過去時制を要求）

表現	意味	ML文脈例文
yesterday	昨日	I fixed the data leak yesterday.
last ~	この前の〜	The bug was introduced last week.
then	そのとき	The model crashed then without warning.
~ ago	～前	We released the dataset two months ago.
when	～だったとき	I noticed the issue when I ran the test.
just now	たった今	The loss dropped just now.

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📘4. 形式主語構文に書き換え可能な形容詞（It is 〜 to V）

形容詞	意味	ML文脈例文（形式主語）
easy	簡単な	It is easy to deploy this model.
impossible	不可能な	It is impossible to train on this device.
convenient	便利な	It is convenient to use a pre-trained model.
difficult/hard	難しい	It is hard to optimize this function.
pleasant	心地よい	It is pleasant to work with a well-documented API.
dangerous	危険な	It is dangerous to ignore data imbalance.
comfortable	快適な	It is comfortable to test in a controlled environment.

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📘1. 相互複数（互いに～する）表現まとめ

表現	意味	ML文脈例文
change trains	乗り換える（電車）	We had to change servers like changing trains during model migration.
make friends (with ~)	～と友達になる	He made friends with researchers at the ML conference.
exchange seats	席を取り替える	The engineers exchanged seats to pair program efficiently.
be on ... terms with ~	～とは…な間柄である	We are on good terms with the open-source community.
shake hands	握手する	They shook hands after agreeing on the API standard.
take turns (at) Ving	交代で～する	We took turns reviewing the training logs overnight.

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📘2. 仮定法現在（that節内で動詞の原形）

仮定法現在とは、主節の動詞にかかわらず、that節で原形動詞または should + V が使われる構文。特に命令・提案・要求・主張を表す動詞の後でよく使われます。

主節の動詞分類と機械学習文脈例文

種類	主節の動詞例	that節（ML例文）
命令	order	I ordered that the script be terminated immediately.
決定	determine, decide	We decided that the pipeline start from scratch.
要求	demand, require, request	They demanded that the model be retrained with new data.
主張	insist	She insisted that the results be verified independently.
提案	suggest, propose, recommend	I suggest that the logs be saved every epoch.

※英語（イギリス式）では "should + V"：I suggest that the model should be evaluated again.
※アメリカ英語では "V原形" が一般的：I suggest that the model be evaluated again.

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