Update README.md

README.md

@@ -41,7 +41,7 @@ This repository provides large language models developed by [LLM-jp](https://llm
 |**Pre-trained models**|
 | [llm-jp-13b-v1.0](https://huggingface.co/llm-jp/llm-jp-13b-v1.0) | 
 | [llm-jp-1.3b-v1.0](https://huggingface.co/llm-jp/llm-jp-1.3b-v1.0) | 
-Checkpoints format: `transformers` (Megatron-DeepSpeed format available [here](https://huggingface.co/llm-jp/llm-jp-13b-v1.0-mdsfmt))
+Checkpoints format: Hugging Face Transformers (Megatron-DeepSpeed format models are available [here](https://huggingface.co/llm-jp/llm-jp-13b-v1.0-mdsfmt))
 
 
 ## Required Libraries and Their Versions
@@ -101,8 +101,8 @@ print(tokenizer.decode(output))
 
 ## Tokenizer
 The tokenizer of this model is based on [huggingface/tokenizers](https://github.com/huggingface/tokenizers) Unigram byte-fallback model.
-The vocab entries were converted from [`llm-jp-tokenizer v2.1 (50k)`](https://github.com/llm-jp/llm-jp-tokenizer/releases/tag/v2.1).
-Please refer to [README.md](https://github.com/llm-jp/llm-jp-tokenizer) of `llm-ja-tokenizer` for the details of vocab constuction steps.
+The vocabulary entries were converted from [`llm-jp-tokenizer v2.1 (50k)`](https://github.com/llm-jp/llm-jp-tokenizer/releases/tag/v2.1).
+Please refer to the [README.md](https://github.com/llm-jp/llm-jp-tokenizer) of `llm-ja-tokenizer` for details on the vocabulary construction procedure.
 - **Model:** Hugging Face Fast Tokenizer using Unigram byte-fallback model which requires `tokenizers>=0.14.0`
 - **Training algorithm:** SentencePiece Unigram byte-fallback
 - **Training data:** A subset of the datasets for model pre-training
@@ -113,7 +113,7 @@ Please refer to [README.md](https://github.com/llm-jp/llm-jp-tokenizer) of `llm-
 
 ### Pre-training
 
-The models have been pre-trained on approximately 287.5B tokens, sourced from a blend of the following datasets.
+The models have been pre-trained using a blend of the following datasets.
 
 | Language | Dataset | Tokens|
 |:---:|:---:|:---:|
@@ -123,7 +123,8 @@ The models have been pre-trained on approximately 287.5B tokens, sourced from a
 ||[The Pile](https://huggingface.co/datasets/EleutherAI/pile)|135B
 |Codes|[The Stack](https://huggingface.co/datasets/bigcode/the-stack)|10B
 
-Pretraining was done by 10-hold shards that consists approx. 27-28B tokens. We further finalized the pretraining with additional cleaned 27B tokens data.
+The pre-training was continuously conducted using a total of 10 folds of non-overlapping data, each consisting of approximately 27-28B tokens.
+We finalized the pre-training with additional (potentially) high-quality 27B tokens data obtained from the identical source data sets listed above used for the 10-fold data.
 
 ### Instruction tuning
 
@@ -157,4 +158,4 @@ llm-jp(at)nii.ac.jp
 ## Model Card Authors
 *The names are listed in alphabetical order.*
 
-Namgi Han, Hirokazu Kiyomaru, Hiroshi Matsuda, Shota Sasaki, Shuhei Kurita, Taishi Nakamura, Takumi Okamoto.
+Hirokazu Kiyomaru, Hiroshi Matsuda, Jun Suzuki, Namgi Han, Saku Sugawara, Shota Sasaki, Shuhei Kurita, Taishi Nakamura, Takumi Okamoto.