llm-jp
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llm-jp-modernbert-base

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 ---
 library_name: transformers
+license: apache-2.0
+language:
+- ja
 ---
+# llm-jp-modernbert-base-v4-ja
+This model is based on the [modernBERT-base](https://arxiv.org/abs/2412.13663) architecture with [llm-jp-tokenizer](https://github.com/llm-jp/llm-jp-tokenizer).
+It was trained using the Japanese subset (3.4TB) of the llm-jp-corpus v4 and supports a max sequence length of 8192.
+## Training
+This model was trained with a max_seq_len of 1024 in stage 1, and then with a max_seq_len of 8192 in stage 2.
+| Model              |      stage 1    |    stage 2      |
+|:------------------ |----------------:|----------------:|
+| max_seq_len        | 1024            | 8192            |
+| max_steps          | 500,000         | 200,000         |
+| Total batch size   | 3328            | 384             |
+| Peak LR            | 5e-4            | 5e-5            |
+| warmup step        | 24,000          |                 |
+| LR schedule        | Linear decay    |                 |
+| Adam beta 1        | 0.9             |                 |
+| Adam beta 2        | 0.98            |                 |
+| Adam eps           | 1e-6            |                 |
+| MLM prob           | 0.30            |                 |
+| Gradient clipping  | 1.0             |                 |
+| weight decay       | 1e-5            |                 |
+| line_by_line       | True            |                 |
+The blank in stage 2 indicate the same value as in stage 1.
+In theory, stage 1 consumes 1.7T tokens, but sentences with fewer than 1024 tokens are truncated, so the actual consumption is lower. Stage 2 theoretically consumes 0.6T tokens.
+For reference, Warner et al.'s ModernBERT uses 1.72T tokens for stage 1, 250B tokens for stage 2, and 50B tokens for stage 3.
 ## Evaluation
+For the sentence classification task evaluation, the datasets JSTS, JNLI, and JCoLA from [JGLUE](https://aclanthology.org/2022.lrec-1.317/) were used. For the evaluation of the Zero-shot Sentence Retrieval task, the [miracl/miracl](https://huggingface.co/datasets/miracl/miracl) dataset (ja subset) was used.
+Evaluation code can be found at https://github.com/speed1313/bert-eval
+| Model                                          |   JSTS |   JNLI |   JCoLA |   Avg(JGLUE) | miracl   |    Avg |
+|------------------------------------------------|--------|--------|---------|--------------|----------|--------|
+| tohoku-nlp/bert-base-japanese-v3               | 0.9196 | 0.9117 |  0.8798 |       0.9037 | 0.74     | 0.8628 |
+| sbintuitions/modernbert-ja-130m                | 0.9159 | 0.9273 |  0.8682 |       0.9038 | 0.5069   | 0.8046 |
+| sbintuitions/modernbert-ja-310m                | 0.9317 | 0.9326 |  0.8832 |       0.9158 | 0.6569   | 0.8511 |
+| llm-jp-modernbert-base-v3-stage1-500k   | 0.9247 | 0.917  |  0.8555 |       0.8991 | 0.5515   | 0.8122 |
+| llm-jp-modernbert-base-v3-stage2-200k   | 0.9238 | 0.9108 |  0.8439 |       0.8928 | 0.5384   | 0.8042 |
+| llm-jp-modernbert-base-v4-ja-stage1-100k | 0.9213 | 0.9182 |  0.8613 |       0.9003 | N/A      | N/A |
+| llm-jp-modernbert-base-v4-ja-stage1-300k | 0.9199 | 0.9187 |  0.852  |       0.8969 | N/A      | N/A |
+| llm-jp-modernbert-base-v4-ja-stage1-400k | 0.9214 | 0.9203 |  0.8555 |       0.8991 | N/A      | N/A |
+| llm-jp-modernbert-base-v4-ja-stage1-500k | 0.9212 | 0.9195 |  0.8451 |       0.8953 | 0.6025   | 0.8221 |
+| llm-jp-modernbert-base-v4-ja-stage2-200k | 0.9177 | 0.9133 |  0.8439 |       0.8916 | 0.5739   | 0.8122 |