update readme

README.md

@@ -1,111 +1,23 @@
 ---
 tags:
-- sentence-transformers
-- feature-extraction
-- sentence-similarity
-- transformers
+  - sentence-transformers
+  - feature-extraction
+  - sentence-similarity
+  - transformers
 datasets:
-- shunk031/jsnli
+  - shunk031/jsnli
 license: cc-by-sa-4.0
 language:
-- ja
+  - ja
 metrics:
-- spearmanr
+  - spearmanr
 pipeline_tag: sentence-similarity
 library_name: generic
 ---
 
-# sup-simcse-ja-base
+sentence-transformers の widget を日本語対応できないか実験しています。
+generic library を実行するために public repo にしています。
 
+pipeline.py, README.md, requirements.txt 以外のファイルは [cl-nagoya/sup-simcse-ja-base](https://huggingface.co/cl-nagoya/sup-simcse-ja-base) (CC BY-SA 4.0) のコピーです。
 
-## 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("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("sup-simcse-ja-base")
-model = AutoModel.from_pretrained("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}}
-}
-```
+（language tag が Japanese なら裏側で `pip install transformer[ja]` をするのが最善に感じますが、contribute できそうな repository が見当たりませんでした。）