D语言现状From2007

作者指出D语言当前版本频繁出现的bug严重影响了代码的稳定性,并建议D语言社区建立稳定版和不稳定版两个分支来解决这一问题。同时,作者还强调了文档中明确版本差异的重要性。

I'm sure this was brought up in the past, but DMD definitely needs stable 
and unstable branches.

-v1 doesn't cut it. My code is compiled with -v1 and still breaks with new 
DMD versions.

Each new DMD version is bug-ridden. This new one 1.011 is pretty bad!

How am I supposed to let others use my code when there's no stability in 
the compiler? They update their compiler and report to me "your code is 
broken"; well, no, DMD is broken.

I have a ton of code that doesn't work on any of the new DMD compilers; I 
have to use an old pre-1.0 compiler, because the recent compilers are 
bug-ridden. Some bugs get fixed, but even more get added.

I'm sure a lot of you out there have similar experiences. Speak up now, 
please!

With each new release I get more and more frustrated with D. There's no 
stability! I know you want more and more features, but how can I keep 
using a language like this?

-

I know, I know, report bugs. This doesn't cut it. Reporting bugs is hard 
as hell and time consuming. I need time to report bugs. Now I have to 
either restrict use to specific compiler versions, which people don't 
always know about and report their issues back to me, until I remind them 
they need to downgrade their compiler (which isn't always an option if 
they need bug fixes), or I have to rush to fix my code to workaround such 
issues and report bugs. If there was a stable branch, I could get the code 
working with the unstable branch at a reasonable pace.

-

D 1.0 means nothing. The 1.0 release was a huge flop. I think it could 
have done so much better and retained more users. We need some stability 
and to try the big release one more time. "D 1.1 release 'whoops, got it 
right this time'" (hopefully).

Also, the documentation should probably clearly state differences between 
versions, perhaps even with the words "unstable" near the things not in 
the stable branch. (Safe to ignore 1.0 since it's pointless.)

-

I've had all this in the back of my mind for quite some time and I've 
tried to be patient about it. I'm not trying threaten anyone, but I don't 
know how much longer I'm going to put up with D with its current methods. 
Note that I am probably one of the oldest D users still using it.

-

Thanks for your time.

- Christopher E. Miller

 

回顾D语言走的崎岖之路1

### BERT模型的研究现状与最新进展 #### 1. BERT及其变种的发展趋势 近年来,随着自然语言处理技术的进步,BERT模型已经成为许多NLP任务的核心基础之一。尽管最初的BERT模型已经取得了显著的成功,但研究人员不断对其进行改进和扩展,以适应更多复杂的场景和需求。 一种重要的发展方向是通过优化预训练策略提高模型性能。例如,RoBERTa通过对掩码策略、训练数据规模以及训练时间等方面的调整,在多项任务中超越了原始的BERT模型[^2]。此外,为了减少计算资源消耗并保持高性能,轻量化版本如ALBERT被提出。该模型采用参数共享机制和其他创新设计降低了内存占用,同时维持了良好的效果[^3]。 #### 2. 领域特定的应用与发展 除了通用型改进外,领域专用版BERT也得到了快速发展。这些定制化版本能够更好地满足特定行业或应用场景下的特殊要求: - 中文环境中的`BERT-base-chinese`专门用于解决汉语相关问题; - 命名实体识别任务中有针对性强的`BERT-base-NER`; - 医疗健康方向则出现了像`Symps_disease_bert_v3_c41`, 它可以作为症状到疾病的分类工具; - 法律和技术文档分析方面也有相应的解决方案比如基于大规模专利语料库构建而成的 `Patent-BERT`. 以上实例表明,根据不同领域的特点微调后的BERT能够在各自的专业范畴内发挥更大作用[^1]. #### 3. 对内部工作机制更深理解的新发现 学术界对于如何进一步挖掘BERT潜力进行了深入探索。一项值得注意的工作来自Tenney等人(2019),他们揭示了大型BERT架构里每一层transformer组件在执行不同类型nlp子任务时所扮演的角色差异性[^4]。这种洞察有助于指导未来关于网络结构修改或者剪枝操作方面的决策制定过程. 另外值得一提的是,在实际应用过程中人们逐渐意识到虽然bert带来了革命性的变革但它并非完美无缺存在一些固有的缺陷需要克服。因此持续关注其局限性和寻找突破点也是当前研究的一个重要组成部分。 ```python # 示例代码展示如何加载预训练好的 HuggingFace Transformers 库中的 BERT 模型 from transformers import BertTokenizer, BertModel tokenizer = BertTokenizer.from_pretrained('bert-base-uncased') model = BertModel.from_pretrained('bert-base-uncased') text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) print(output.last_hidden_state.shape) ```
评论
成就一亿技术人!
拼手气红包6.0元
还能输入1000个字符
 
红包 添加红包
表情包 插入表情
 条评论被折叠 查看
添加红包

请填写红包祝福语或标题

红包个数最小为10个

红包金额最低5元

当前余额3.43前往充值 >
需支付:10.00
成就一亿技术人!
领取后你会自动成为博主和红包主的粉丝 规则
hope_wisdom
发出的红包
实付
使用余额支付
点击重新获取
扫码支付
钱包余额 0

抵扣说明:

1.余额是钱包充值的虚拟货币,按照1:1的比例进行支付金额的抵扣。
2.余额无法直接购买下载,可以购买VIP、付费专栏及课程。

余额充值