|
--- |
|
tags: |
|
- sentence-transformers |
|
- feature-extraction |
|
- sentence-similarity |
|
- transformers |
|
datasets: |
|
- shunk031/jsnli |
|
license: cc-by-sa-4.0 |
|
language: |
|
- ja |
|
metrics: |
|
- spearmanr |
|
library_name: sentence-transformers |
|
inference: false |
|
--- |
|
|
|
# sup-simcse-ja-base |
|
|
|
|
|
## Usage (Sentence-Transformers) |
|
|
|
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: |
|
|
|
``` |
|
pip install -U fugashi[unidic-lite] sentence-transformers |
|
``` |
|
|
|
Then you can use the model like this: |
|
|
|
```python |
|
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("cl-nagoya/sup-simcse-ja-base") |
|
embeddings = model.encode(sentences) |
|
print(embeddings) |
|
``` |
|
|
|
## Usage (HuggingFace Transformers) |
|
Without [sentence-transformers](https://www.SBERT.net), 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. |
|
|
|
```python |
|
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("cl-nagoya/sup-simcse-ja-base") |
|
model = AutoModel.from_pretrained("cl-nagoya/sup-simcse-ja-base") |
|
|
|
# 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) |
|
``` |
|
|
|
## Full Model Architecture |
|
``` |
|
SentenceTransformer( |
|
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel |
|
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) |
|
) |
|
``` |
|
|
|
## Model Summary |
|
|
|
- Fine-tuning method: Supervised SimCSE |
|
- Base model: [cl-tohoku/bert-base-japanese-v3](https://huggingface.co/cl-tohoku/bert-base-japanese-v3) |
|
- Training dataset: [JSNLI](https://nlp.ist.i.kyoto-u.ac.jp/?%E6%97%A5%E6%9C%AC%E8%AA%9ESNLI%28JSNLI%29%E3%83%87%E3%83%BC%E3%82%BF%E3%82%BB%E3%83%83%E3%83%88) |
|
- Pooling strategy: cls (with an extra MLP layer only during training) |
|
- Hidden size: 768 |
|
- Learning rate: 5e-5 |
|
- Batch size: 512 |
|
- Temperature: 0.05 |
|
- Max sequence length: 64 |
|
- Number of training examples: 2^20 |
|
- Validation interval (steps): 2^6 |
|
- Warmup ratio: 0.1 |
|
- Dtype: BFloat16 |
|
|
|
See the [GitHub repository](https://github.com/hppRC/simple-simcse-ja) for a detailed experimental setup. |
|
|
|
## Citing & Authors |
|
|
|
``` |
|
@misc{ |
|
hayato-tsukagoshi-2023-simple-simcse-ja, |
|
author = {Hayato Tsukagoshi}, |
|
title = {Japanese Simple-SimCSE}, |
|
year = {2023}, |
|
publisher = {GitHub}, |
|
journal = {GitHub repository}, |
|
howpublished = {\url{https://github.com/hppRC/simple-simcse-ja}} |
|
} |
|
``` |
