metadata

language:
  - ja
pipeline_tag: sentence-similarity
tags:
  - sentence-transformers
  - feature-extraction
  - sentence-similarity
  - transformers
inference: false
datasets:
  - shunk031/JGLUE
  - shunk031/jsnli
  - hpprc/jsick
  - miracl/miracl
  - castorini/mr-tydi
  - unicamp-dl/mmarco
library_name: sentence-transformers

fio-base-japanese-v0.1

日本語版は近日公開予定です（日本語を勉強中なので、間違いはご容赦ください！）

fio-base-japanese-v0.1 is a proof of concept, and the first release of the Fio family of Japanese embeddings. It is based on cl-tohoku/bert-base-japanese-v3 and trained on limited volumes of data on a single GPU.

For more information, please refer to my notes on Fio.

Datasets

Similarity/Entailment:

JSTS (train)
JSNLI (train)
JNLI (train)
JSICK (train)

Retrieval:

MMARCO (Multilingual Marco) (train, 124k sentence pairs, <1% of the full data)
Mr.TyDI (train)
MIRACL (train, 50% sample)
~~JSQuAD (train, 50% sample, no LLM enhancement)~~ JSQuAD is not used in the released version, to serve as an unseen test set.

Results

⚠️ WARNING: fio-base-japanese-v0.1 has seen textual entailment tasks during its training, which is not the case of the other other japanese-only models in this table. This gives Fio an unfair advantage over the previous best results, cl-nagoya/sup-simcse-ja-[base|large]. During mid-training evaluations, this didn't seem to greatly affect performance, however, JSICK (NLI set) was included in the training data, and therefore it's impossible to fully remove this contamination at the moment. I intend to fix this in future release, but please keep this in mind as you view the results (see JSQuAD results on the associated blog post for a fully unseen comparison, although focused on retrieval).

This is adapted and truncated (to keep only the most popular models) from oshizo's benchmarking github repo, please check it out for more information and give it a star as it was very useful!

Italic denotes best model for its size when a smaller model outperforms a bigger one (base/large | 768/1024), bold denotes best overall.

Model	JSTS valid-v1.1	JSICK test	MIRACL dev	Average
bclavie/fio-base-japanese-v0.1	*0.863*	*0.894*	0.718	0.825
cl-nagoya/sup-simcse-ja-base	0.809	0.827	0.527	0.721
cl-nagoya/sup-simcse-ja-large	0.831	0.831	0.507	0.723
colorfulscoop/sbert-base-ja	0.742	0.657	0.254	0.551
intfloat/multilingual-e5-base	0.796	0.806	0.845	0.816
intfloat/multilingual-e5-large	0.819	0.794	0.883	*0.832*
pkshatech/GLuCoSE-base-ja	0.818	0.757	0.692	0.755
text-embedding-ada-002	0.790	0.789	0.7232	0.768

Usage

This model requires both fugashi and unidic-lite:

pip install -U fugashi unidic-lite

If using for a retrieval task, you must prefix your query with "関連記事を取得するために使用できるこの文の表現を生成します: ".

Usage (Sentence-Transformers)

This model is best used through sentence-transformers. If you don't have it, it's easy to install:

pip install -U sentence-transformers

Then you can use the model like this:

from sentence_transformers import SentenceTransformer
sentences = ["こんにちは、世界！", "文埋め込み最高！文埋め込み最高と叫びなさい", "極度乾燥しなさい"]

model = SentenceTransformer('bclavie/fio-base-japanese-v0.1')
embeddings = model.encode(sentences)
print(embeddings)

Usage (HuggingFace Transformers)

Without sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.

from transformers import AutoTokenizer, AutoModel
import torch


def cls_pooling(model_output, attention_mask):
    return model_output[0][:,0]


# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']

# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')

# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')

# Compute token embeddings
with torch.no_grad():
    model_output = model(**encoded_input)

# Perform pooling. In this case, cls pooling.
sentence_embeddings = cls_pooling(model_output, encoded_input['attention_mask'])

print("Sentence embeddings:")
print(sentence_embeddings)

Citing & Authors

  bclavie-fio-embeddings,
  author = {Benjamin Clavié},
  title = {Fio Japanese Embeddings},
  year = {2023},
  howpublished = {\url{https://ben.clavie.eu/fio}}
}```