japanese-large-lm-3.6b
Maintainer: line-corporation
74
🛸
| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
Create account to get full access
Model overview
The japanese-large-lm-3.6b is a 3.6 billion parameter Japanese language model trained by LINE Corporation. It is a GPT-style model with 24 layers, a 2304 hidden dimension, and 24 attention heads. The model was trained on a corpus of approximately 650 GB of text data, including the Japanese portions of datasets like C4, CC-100, and Oscar. Compared to similar Japanese language models like the japanese-gpt-neox-3.6b and japanese-gpt-1b, the japanese-large-lm-3.6b has a larger model size and was trained on a more diverse set of data.
Model inputs and outputs
Inputs
- Raw Japanese text to be processed and used as input for language generation.
Outputs
- Continuation of the input text, generating new Japanese text based on the model's learned patterns and understanding of the language.
Capabilities
The japanese-large-lm-3.6b model is capable of generating coherent and contextually appropriate Japanese text. It can be used for a variety of language-related tasks, such as:
- Text completion: Given a partial sentence, the model can generate the rest of the text.
- Language modeling: The model can be used to evaluate the likelihood of a given piece of Japanese text, which can be useful for tasks like language understanding and translation.
- Text generation: The model can be used to generate novel Japanese text, which can be useful for creative writing, dialogue generation, and other applications.
What can I use it for?
The japanese-large-lm-3.6b model can be used for a wide range of Japanese language-related applications, such as:
- Chatbots and virtual assistants: The model can be fine-tuned to engage in natural conversations in Japanese.
- Content generation: The model can be used to generate Japanese articles, stories, or other types of text content.
- Language learning: The model can be used to generate Japanese text for language learners to practice reading and comprehension.
- Machine translation: The model can be used as a component in a larger machine translation system, helping to generate fluent Japanese output.
Things to try
One interesting aspect of the japanese-large-lm-3.6b model is its ability to capture the nuances and complexities of the Japanese language. Compared to smaller Japanese language models, this larger model may be able to better handle things like honorifics, regional dialects, and idiomatic expressions. Developers could experiment with prompting the model with various types of Japanese text, such as formal documents, casual conversations, or literary passages, to see how it handles the different styles and registers.
Another area to explore would be using the model for Japanese language understanding tasks, such as question answering or textual entailment. The model's strong performance on the Japanese portions of benchmarks like JGLUE suggests it may be a powerful foundation for building more advanced natural language processing capabilities in Japanese.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
Related Models
🏋️
japanese-gpt-neox-3.6b
88
The japanese-gpt-neox-3.6b is a 3.6 billion parameter Japanese language model developed by rinna. The model was trained using the EleutherAI/gpt-neox codebase on a dataset of over 312.5 billion Japanese tokens from sources like Japanese CC-100, Japanese C4, and Japanese Wikipedia. This results in a model with a validation perplexity of 8.68. The model comes in several variants, including an instruction-following fine-tuned version (rinna/japanese-gpt-neox-3.6b-instruction-sft) and a reinforcement learning version (rinna/japanese-gpt-neox-3.6b-instruction-ppo). These variants allow the model to better understand and follow human instructions. In comparison, the gpt-neox-20b model is a 20 billion parameter English language model trained by EleutherAI, while the mGPT model is a 1.3 billion parameter multilingual model developed by AI-Forever covering 61 languages. The gpt-j-6b model is a 6 billion parameter English language model developed by EleutherAI. Model Inputs and Outputs Inputs Text prompts in Japanese for the model to continue and generate additional text. Outputs Continued Japanese text generated by the model based on the input prompt. Capabilities The japanese-gpt-neox-3.6b model can be used for a variety of Japanese language tasks, such as text generation, summarization, translation, and question answering. The model's strong performance on the Japanese language corpus allows it to generate coherent and contextually relevant Japanese text. The fine-tuned variants of the model, like rinna/japanese-gpt-neox-3.6b-instruction-sft, demonstrate an even stronger ability to understand and follow human instructions, making them useful for building interactive Japanese language assistants or chatbots. What Can I Use It For? The japanese-gpt-neox-3.6b model can be a valuable tool for Japanese language researchers and developers. It can be used as a base model for fine-tuning on specific Japanese language tasks, or as a starting point for developing personalized Japanese language applications. For example, a Japanese language tutoring app could use the model to generate natural Japanese responses to student prompts, providing an immersive language learning experience. Alternatively, a Japanese e-commerce platform could leverage the model's text generation capabilities to automatically produce product descriptions and summaries. The instruction-following variants of the model, like rinna/japanese-gpt-neox-3.6b-instruction-sft, could be used to build sophisticated Japanese language assistants that can understand and execute complex user requests. Things to Try One interesting aspect of the japanese-gpt-neox-3.6b model is its ability to generate coherent and contextually relevant Japanese text. Try providing the model with a Japanese sentence or paragraph as a prompt and see how it continues the text. Observe how the model maintains the style, tone, and overall coherence of the generated output. You can also experiment with the different variants of the model, like rinna/japanese-gpt-neox-3.6b-instruction-sft, and compare their performance on tasks that require understanding and following human instructions. This can give you insights into the model's robustness and potential applications.
Updated Invalid Date
🤷
weblab-10b
63
The weblab-10b is a Japanese-centric multilingual GPT-NeoX model with 10 billion parameters, developed by matsuo-lab. It was trained on a mixture of the Japanese C4 and The Pile datasets, totaling around 600 billion tokens. The model architecture consists of 36 layers and a 4864-hidden size, making it a large and powerful language model. Similar models in the series include the weblab-10b-instruction-sft variant, which has been fine-tuned for instruction-following. Model inputs and outputs The weblab-10b model takes in text as input and generates text as output, making it a versatile text-to-text language model. It can be used for a variety of natural language processing tasks, such as text generation, language understanding, and language translation. Inputs Text prompt: The model accepts arbitrary text as input, which it then uses to generate additional text. Outputs Generated text: The model outputs generated text that continues or responds to the input prompt. The length and content of the output can be controlled through various generation parameters. Capabilities The weblab-10b model has demonstrated strong performance on a range of Japanese language tasks, including commonsense question answering, natural language inference, and summarization. Its large scale and multilingual nature make it a powerful tool for working with Japanese language data. What can I use it for? The weblab-10b model can be used for a variety of applications, such as: Text generation**: The model can be used to generate coherent and context-appropriate Japanese text, which can be useful for tasks like creative writing, dialogue generation, or report summarization. Language understanding**: By fine-tuning the model on specific tasks, it can be used to improve performance on a range of Japanese NLP tasks, such as question answering or text classification. Multilingual applications**: The model's multilingual capabilities can be leveraged for applications that require translation or cross-lingual understanding. Things to try One interesting aspect of the weblab-10b model is its strong performance on Japanese language tasks, which highlights its potential for working with Japanese data. Researchers and developers could explore fine-tuning the model on domain-specific Japanese datasets to tackle specialized problems, or investigating its ability to generate coherent and contextually appropriate Japanese text. Another area to explore is the model's multilingual capabilities and how they can be leveraged for cross-lingual applications. Experiments could involve testing the model's ability to understand and generate text in multiple languages, or exploring zero-shot or few-shot learning approaches for tasks like machine translation. Overall, the weblab-10b model represents a powerful and flexible language model that can be a valuable tool for a wide range of Japanese and multilingual NLP applications.
Updated Invalid Date
💬
bert-base-japanese-v3
43
The bert-base-japanese-v3 model is a Japanese language model based on the BERT architecture, developed by the tohoku-nlp team. It is trained on a large corpus of Japanese text, including the Japanese portion of the CC-100 dataset and the Japanese Wikipedia. The model uses word-level tokenization based on the Unidic 2.1.2 dictionary, followed by WordPiece subword tokenization. It is trained with whole word masking, where all subword tokens corresponding to a single word are masked at once during pretraining. This model can be compared to other Japanese BERT models like bert-base-japanese-whole-word-masking, which also uses whole word masking, and the multilingual bert-base-multilingual-uncased model, which covers 102 languages including Japanese. Model inputs and outputs Inputs Text**: The bert-base-japanese-v3 model takes in Japanese text as input, which is first tokenized using the Unidic 2.1.2 dictionary and then split into subwords using the WordPiece algorithm. Outputs Token representations**: The model outputs contextual representations for each token in the input text, which can be used for a variety of downstream natural language processing tasks. Capabilities The bert-base-japanese-v3 model is a powerful language model that can be fine-tuned for a wide range of Japanese natural language processing tasks, such as text classification, named entity recognition, and question answering. Its whole word masking approach during pretraining allows the model to better capture the semantics of Japanese words, which are often composed of multiple characters. What can I use it for? The bert-base-japanese-v3 model can be used as a starting point for building Japanese language applications, such as: Text classification**: Classify Japanese text into different categories (e.g., sentiment analysis, topic classification). Named entity recognition**: Identify and extract named entities (e.g., people, organizations, locations) from Japanese text. Question answering**: Build systems that can answer questions based on Japanese text passages. To use the model, you can leverage the Hugging Face Transformers library, which provides easy-to-use APIs for fine-tuning and deploying BERT-based models. Things to try One interesting thing to try with the bert-base-japanese-v3 model is to compare its performance on Japanese language tasks to the performance of other Japanese language models, such as bert-base-japanese-whole-word-masking or the multilingual bert-base-multilingual-uncased model. This could help you understand the trade-offs and advantages of the different approaches to pretraining and tokenization used by these models.
Updated Invalid Date
🤷
japanese-gpt-1b
92
The japanese-gpt-1b model is a 1.3 billion parameter Japanese language model developed by rinna Co., Ltd. It is a 24-layer, 2048-hidden-size transformer-based language model trained on Japanese C4, Japanese CC-100, and Japanese Wikipedia data. The model achieves around 14 perplexity on a validation set. Similar Japanese language models from rinna include the japanese-gpt2-medium and the japanese-gpt-neox-3.6b models. The japanese-gpt2-medium is a medium-sized 24-layer, 1024-hidden-size GPT-2 model, while the japanese-gpt-neox-3.6b is a much larger 36-layer, 2816-hidden-size GPT-NeoX model. Model inputs and outputs The japanese-gpt-1b model takes in text as input and generates new text as output. The model uses a sentencepiece-based tokenizer with a vocabulary size of around 32,000 tokens. The tokenizer can handle Japanese text without producing many unknown tokens. Inputs Raw Japanese text Outputs Continuation of the input text, generated one token at a time Capabilities The japanese-gpt-1b model can be used for a variety of Japanese language generation tasks, such as text summarization, question answering, and creative writing. The model's strong performance on the validation set suggests it has learned a good understanding of the Japanese language. What can I use it for? The japanese-gpt-1b model could be used as a starting point for building Japanese language applications, such as chatbots, virtual assistants, or text generation tools. Its large size and strong language modeling capabilities make it suitable for a wide range of Japanese NLP tasks. Things to try Some interesting things to try with the japanese-gpt-1b model include: Fine-tuning the model on a specific Japanese dataset or task to specialize its capabilities Experimenting with different decoding strategies, such as top-k sampling or beam search, to generate more diverse or coherent output Combining the model with other Japanese NLP components, such as named entity recognition or sentiment analysis, to build more complex applications Overall, the japanese-gpt-1b model provides a powerful foundation for working with the Japanese language and offers many opportunities for further exploration and development.
Updated Invalid Date