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--- |
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tags: |
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- sentence-transformers |
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- feature-extraction |
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- sentence-similarity |
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- transformers |
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datasets: |
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- wiki40b |
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license: cc-by-sa-4.0 |
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language: |
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- ja |
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metrics: |
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- spearmanr |
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library_name: sentence-transformers |
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inference: false |
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--- |
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# unsup-simcse-ja-large |
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## Usage (Sentence-Transformers) |
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Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: |
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``` |
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pip install -U fugashi[unidic-lite] sentence-transformers |
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``` |
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Then you can use the model like this: |
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```python |
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from sentence_transformers import SentenceTransformer |
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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"] |
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model = SentenceTransformer("unsup-simcse-ja-large") |
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embeddings = model.encode(sentences) |
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print(embeddings) |
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``` |
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## Usage (HuggingFace Transformers) |
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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. |
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```python |
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from transformers import AutoTokenizer, AutoModel |
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import torch |
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def cls_pooling(model_output, attention_mask): |
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return model_output[0][:,0] |
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# Sentences we want sentence embeddings for |
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sentences = ['This is an example sentence', 'Each sentence is converted'] |
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# Load model from HuggingFace Hub |
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tokenizer = AutoTokenizer.from_pretrained("unsup-simcse-ja-large") |
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model = AutoModel.from_pretrained("unsup-simcse-ja-large") |
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# Tokenize sentences |
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encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') |
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# Compute token embeddings |
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with torch.no_grad(): |
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model_output = model(**encoded_input) |
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# Perform pooling. In this case, cls pooling. |
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sentence_embeddings = cls_pooling(model_output, encoded_input['attention_mask']) |
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print("Sentence embeddings:") |
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print(sentence_embeddings) |
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``` |
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## Full Model Architecture |
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``` |
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SentenceTransformer( |
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(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel |
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(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}) |
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) |
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``` |
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## Model Summary |
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- Fine-tuning method: Unsupervised SimCSE |
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- Base model: [cl-tohoku/bert-large-japanese-v2](https://huggingface.co/cl-tohoku/bert-large-japanese-v2) |
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- Training dataset: [Wiki40B](https://huggingface.co/datasets/wiki40b) |
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- Pooling strategy: cls (with an extra MLP layer only during training) |
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- Hidden size: 1024 |
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- Learning rate: 3e-5 |
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- Batch size: 64 |
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- Temperature: 0.05 |
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- Max sequence length: 64 |
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- Number of training examples: 2^20 |
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- Validation interval (steps): 2^6 |
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- Warmup ratio: 0.1 |
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- Dtype: BFloat16 |
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See the [GitHub repository](https://github.com/hppRC/simple-simcse-ja) for a detailed experimental setup. |
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## Citing & Authors |
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``` |
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@misc{ |
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hayato-tsukagoshi-2023-simple-simcse-ja, |
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author = {Hayato Tsukagoshi}, |
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title = {Japanese Simple-SimCSE}, |
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year = {2023}, |
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publisher = {GitHub}, |
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journal = {GitHub repository}, |
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howpublished = {\url{https://github.com/hppRC/simple-simcse-ja}} |
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} |
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``` |
