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1 Parent(s): d9de6b3

Add new SentenceTransformer model.

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.gitattributes CHANGED
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+ pytorch_model.bin filter=lfs diff=lfs merge=lfs -text
1_Pooling/config.json ADDED
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+ {
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+ "word_embedding_dimension": 768,
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+ "pooling_mode_cls_token": true,
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+ "pooling_mode_mean_tokens": false,
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+ "pooling_mode_max_tokens": false,
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+ "pooling_mode_weightedmean_tokens": false,
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+ "pooling_mode_lasttoken": false
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+ }
README.md ADDED
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+ ---
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+ pipeline_tag: sentence-similarity
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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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+ - shunk031/jsnli
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+ ---
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+
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+ # cl-nagoya/sup-simcse-ja-base
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+
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+ This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
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+
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+ <!--- Describe your model here -->
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+
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+ ## Usage (Sentence-Transformers)
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+
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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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+ ```
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+ pip install -U sentence-transformers
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+ ```
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+
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+ Then you can use the model like this:
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+
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+ ```python
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+ from sentence_transformers import SentenceTransformer
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+ sentences = ["This is an example sentence", "Each sentence is converted"]
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+
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+ model = SentenceTransformer('cl-nagoya/sup-simcse-ja-base')
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+ embeddings = model.encode(sentences)
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+ print(embeddings)
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+ ```
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+
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+
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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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+
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+ ```python
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+ from transformers import AutoTokenizer, AutoModel
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+ import torch
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+
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+
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+ def cls_pooling(model_output, attention_mask):
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+ return model_output[0][:,0]
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+
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+
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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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+
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+ # Load model from HuggingFace Hub
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+ tokenizer = AutoTokenizer.from_pretrained('cl-nagoya/sup-simcse-ja-base')
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+ model = AutoModel.from_pretrained('cl-nagoya/sup-simcse-ja-base')
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+
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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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+
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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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+
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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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+
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+ print("Sentence embeddings:")
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+ print(sentence_embeddings)
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+ ```
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+
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+
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+
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+ ## Evaluation Results
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+
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+ <!--- Describe how your model was evaluated -->
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+
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+ For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=cl-nagoya/sup-simcse-ja-base)
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+
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+
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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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+
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+ ## Citing & Authors
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+
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+ <!--- Describe where people can find more information -->
config.json ADDED
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+ {
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+ "_name_or_path": "./outputs/sup-simcse/jsnli/cl-tohoku__bert-base-japanese-v3/2023-10-02/16-22-36/",
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+ "architectures": [
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+ "BertModel"
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+ ],
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+ "attention_probs_dropout_prob": 0.1,
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+ "hidden_act": "gelu",
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+ "layer_norm_eps": 1e-12,
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+ "model_type": "bert",
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+ "position_embedding_type": "absolute",
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+ "torch_dtype": "float32",
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+ "transformers_version": "4.28.1",
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+ "type_vocab_size": 2,
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+ "use_cache": true,
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+ "vocab_size": 32768
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+ }
config_sentence_transformers.json ADDED
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+ {
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+ "__version__": {
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+ "sentence_transformers": "2.2.2",
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+ "transformers": "4.28.1",
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+ "pytorch": "2.0.1+cu118"
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+ }
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+ }
modules.json ADDED
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+ [
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+ {
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+ "idx": 0,
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+ "name": "0",
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+ "path": "",
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+ "type": "sentence_transformers.models.Transformer"
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+ },
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+ {
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+ "idx": 1,
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+ "name": "1",
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+ "path": "1_Pooling",
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+ "type": "sentence_transformers.models.Pooling"
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+ }
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+ ]
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sentence_bert_config.json ADDED
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+ {
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+ "max_seq_length": 512,
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+ "do_lower_case": false
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+ }
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+ {
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+ "cls_token": "[CLS]",
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tokenizer_config.json ADDED
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+ {
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+ "clean_up_tokenization_spaces": true,
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+ "tokenizer_class": "BertJapaneseTokenizer",
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+ "unk_token": "[UNK]",
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+ "word_tokenizer_type": "mecab"
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+ }
vocab.txt ADDED
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