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retrieva-jp/
t5-large-long

Name: t5-large-long
Availability: InStock

text-generation-inference

endpoints_compatible

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Model card for model ID

This is a T5 v1.1 model, pre-trained on a Japanese corpus.

Model details

T5 is a Transformer-based Encoder-Decoder model, now in v1.1, with the following improvements over the original T5.

GEGLU activation in feed-forward hidden layer, rather than ReLU - see https://arxiv.org/abs/2002.05202 .
Dropout was turned off in pre-training (quality win). Dropout should be re-enabled during fine-tuning.
no parameter sharing between embedding and classifier layer
"xl" and "xxl" replace "3B" and "11B". The model shapes are a bit different - larger d_model and smaller num_heads and d_ff.

This model is based on T5 v1.1. It was pre-trained on a Japanese corpus. For the Japanese corpus, Japanese Wikipedia and mC4/ja were used.

Model Description

Developed by: Retrieva, Inc.
Model type: T5 v1.1
Language(s) (NLP): Japanese
License: CC-BY-SA 4.0 Although commercial use is permitted, we kindly request that you contact us beforehand.

Training Details

We use T5X (https://github.com/google-research/t5x) for the training of this model, and it has been converted to the Huggingface transformer format.

Training Data

The training data used is

The Japanese part of the multilingual C4(mC4/ja).
Japanese Wikipedia(20220920).

Preprocessing

The following filtering is done

Remove documents that do not use a single hiragana character. This removes English-only documents and documents in Chinese.
Whitelist-style filtering using the top level domain of URL to remove affiliate sites.

Training Hyperparameters

dropout rate: 0.0
batch size: 256
fp32
input length: 512
output length: 114
Otherwise, the default value of T5X (https://github.com/google-research/t5x/blob/main/t5x/examples/t5/t5_1_1/large.gin) is followed, including the following.
- optimizer: Adafactor
- base_learning_rate: 1.0
- warmup steps: 10000

Speeds, Sizes, Times

We trained 2097152 steps.

Technical Specifications

Model Architecture and Objective

Model architecture.

T5 v1.1(https://github.com/google-research/text-to-text-transfer-transformer/blob/main/released_checkpoints.md#t511)
Size: Large(~770 million parameters)

Compute Infrastructure

Google Cloud TPU v3-32.

Software

T5X(https://github.com/google-research/t5x).

More Information

https://note.com/retrieva/n/n7b4186dc5ada (in Japanese)

Model Card Authors

Jiro Nishitoba

Model Card Contact

pr@retrieva.jp

retrieva-jp/t5-large-long