一、用方框中所给词的适当形式填空。 每词限用一次。alone, hit, break, foot, train1. After three months of, his young sister could swim.2.Bad luck! The new watch I bought last week3. Martin bought a pair of new sports shoes, but they were bad for his4. A white car a black car ...
【题目】【词汇换装】6. train →___(n.)7. drop →___(过去式)→___(过去分词)8. hit →___(过去式)→___(过去分词)9. ag
Dungeon dwellers, for those who don’t know, are people who choose to train at home. Whether it’s in your basement, garage or wherever, the point is that you choose to do your training at home instead of in a gym. I myself have joined the legions of dungeon dwellers and, while I...
lively coastal areas. You can experience guided tours in cool places like Canberra and Coffs Harbour, all while kicking back in the train’s comfortable cabins. Plus, when you hit Brisbane, there are extra adventures waiting, like checking out Moreton Island or saying g’day at Australia Zoo....
RWKV (pronounced RwaKuv) is an RNN with great LLM performance, which can also be directly trained like a GPT transformer (parallelizable). We are at RWKV-7 "Goose". So it's combining the best of RNN and transformer - great performance, linear time, const
结果1 题目 One day, I took a taxi to the train station.We were driving on the road when suddenly a black car jumped out of another lane(车道) right in front of us. My taxi almost hit the car.The driver of the black car started shouting at us. But the taxi driver just smiled ...
【解析】 1. feet; on foot 2. stomachache; have a stomachache 3. knives 4. lay; lain; lying 5. hurt; hurting; hurtful 6. hit; hitting 7. meaning; meaningful; meaningless 8. satisfaction; satisfied; satisfying; be satisfied with 9. imagination; imagine doing sth. 10. opening; cl...
The National Transportation Safety Board has learned that signals and warning systems were working properly, and a Metro-North train was going below the speed limit, when it struck a sport-utility vehicle in an accident that left six people dead.
We unified the interfaces of instruction-tuning data (e.g., CoT data), multiple LLMs and parameter-efficient methods (e.g., lora, p-tuning) together for easy use. We welcome open-source enthusiasts to initiate any meaningful PR on this repo and integrate
A model that can predict the likelihood of a fake tweet which might have been written by a machine in a given set of tweets while also validating the same on news articles scraped from the internet - Understanding-Machine-vs-Human-Generated-Text-in-News/