初始化项目，由ModelHub XC社区提供模型

Model: setu4993/LEALLA-base
Source: Original Platform

.gitattributes

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README.md

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+---
+pipeline_tag: sentence-similarity
+language:
+  - af
+  - am
+  - ar
+  - as
+  - az
+  - be
+  - bg
+  - bn
+  - bo
+  - bs
+  - ca
+  - ceb
+  - co
+  - cs
+  - cy
+  - da
+  - de
+  - el
+  - en
+  - eo
+  - es
+  - et
+  - eu
+  - fa
+  - fi
+  - fr
+  - fy
+  - ga
+  - gd
+  - gl
+  - gu
+  - ha
+  - haw
+  - he
+  - hi
+  - hmn
+  - hr
+  - ht
+  - hu
+  - hy
+  - id
+  - ig
+  - is
+  - it
+  - ja
+  - jv
+  - ka
+  - kk
+  - km
+  - kn
+  - ko
+  - ku
+  - ky
+  - la
+  - lb
+  - lo
+  - lt
+  - lv
+  - mg
+  - mi
+  - mk
+  - ml
+  - mn
+  - mr
+  - ms
+  - mt
+  - my
+  - ne
+  - nl
+  - no
+  - ny
+  - or
+  - pa
+  - pl
+  - pt
+  - ro
+  - ru
+  - rw
+  - si
+  - sk
+  - sl
+  - sm
+  - sn
+  - so
+  - sq
+  - sr
+  - st
+  - su
+  - sv
+  - sw
+  - ta
+  - te
+  - tg
+  - th
+  - tk
+  - tl
+  - tr
+  - tt
+  - ug
+  - uk
+  - ur
+  - uz
+  - vi
+  - wo
+  - xh
+  - yi
+  - yo
+  - zh
+  - zu
+tags:
+  - bert
+  - sentence_embedding
+  - multilingual
+  - google
+  - sentence-similarity
+  - lealla
+  - labse
+license: apache-2.0
+datasets:
+  - CommonCrawl
+  - Wikipedia
+---
+
+# LEALLA-base
+
+## Model description
+
+LEALLA is a collection of lightweight language-agnostic sentence embedding models supporting 109 languages, distilled from [LaBSE](https://ai.googleblog.com/2020/08/language-agnostic-bert-sentence.html). The model is useful for getting multilingual sentence embeddings and for bi-text retrieval.
+
+- Model: [HuggingFace's model hub](https://huggingface.co/setu4993/LEALLA-base).
+- Paper: [arXiv](https://arxiv.org/abs/2302.08387).
+- Original model: [TensorFlow Hub](https://tfhub.dev/google/LEALLA/LEALLA-base/1).
+- Conversion from TensorFlow to PyTorch: [GitHub](https://github.com/setu4993/convert-labse-tf-pt).
+
+This is migrated from the v1 model on the TF Hub. The embeddings produced by both the versions of the model are [equivalent](https://github.com/setu4993/convert-labse-tf-pt/blob/c0d4fbce789b0709a9664464f032d2e9f5368a86/tests/test_conversion_lealla.py#L31). Though, for some of the languages (like Japanese), the LEALLA models appear to require higher tolerances when comparing embeddings and similarities.
+
+## Usage
+
+Using the model:
+
+```python
+import torch
+from transformers import BertModel, BertTokenizerFast
+
+
+tokenizer = BertTokenizerFast.from_pretrained("setu4993/LEALLA-base")
+model = BertModel.from_pretrained("setu4993/LEALLA-base")
+model = model.eval()
+
+english_sentences = [
+    "dog",
+    "Puppies are nice.",
+    "I enjoy taking long walks along the beach with my dog.",
+]
+english_inputs = tokenizer(english_sentences, return_tensors="pt", padding=True)
+
+with torch.no_grad():
+    english_outputs = model(**english_inputs)
+```
+
+To get the sentence embeddings, use the pooler output:
+
+```python
+english_embeddings = english_outputs.pooler_output
+```
+
+Output for other languages:
+
+```python
+italian_sentences = [
+    "cane",
+    "I cuccioli sono carini.",
+    "Mi piace fare lunghe passeggiate lungo la spiaggia con il mio cane.",
+]
+japanese_sentences = ["犬", "子犬はいいです", "私は犬と一緒にビーチを散歩するのが好きです"]
+italian_inputs = tokenizer(italian_sentences, return_tensors="pt", padding=True)
+japanese_inputs = tokenizer(japanese_sentences, return_tensors="pt", padding=True)
+
+with torch.no_grad():
+    italian_outputs = model(**italian_inputs)
+    japanese_outputs = model(**japanese_inputs)
+
+italian_embeddings = italian_outputs.pooler_output
+japanese_embeddings = japanese_outputs.pooler_output
+```
+
+For similarity between sentences, an L2-norm is recommended before calculating the similarity:
+
+```python
+import torch.nn.functional as F
+
+
+def similarity(embeddings_1, embeddings_2):
+    normalized_embeddings_1 = F.normalize(embeddings_1, p=2)
+    normalized_embeddings_2 = F.normalize(embeddings_2, p=2)
+    return torch.matmul(
+        normalized_embeddings_1, normalized_embeddings_2.transpose(0, 1)
+    )
+
+
+print(similarity(english_embeddings, italian_embeddings))
+print(similarity(english_embeddings, japanese_embeddings))
+print(similarity(italian_embeddings, japanese_embeddings))
+```
+
+## Details
+
+Details about data, training, evaluation and performance metrics are available in the [original paper](https://arxiv.org/abs/2302.08387).
+
+### BibTeX entry and citation info
+
+```bibtex
+@inproceedings{mao-nakagawa-2023-lealla,
+    title = "{LEALLA}: Learning Lightweight Language-agnostic Sentence Embeddings with Knowledge Distillation",
+    author = "Mao, Zhuoyuan  and
+      Nakagawa, Tetsuji",
+    booktitle = "Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics",
+    month = may,
+    year = "2023",
+    address = "Dubrovnik, Croatia",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2023.eacl-main.138",
+    doi = "10.18653/v1/2023.eacl-main.138",
+    pages = "1886--1894",
+    abstract = "Large-scale language-agnostic sentence embedding models such as LaBSE (Feng et al., 2022) obtain state-of-the-art performance for parallel sentence alignment. However, these large-scale models can suffer from inference speed and computation overhead. This study systematically explores learning language-agnostic sentence embeddings with lightweight models. We demonstrate that a thin-deep encoder can construct robust low-dimensional sentence embeddings for 109 languages. With our proposed distillation methods, we achieve further improvements by incorporating knowledge from a teacher model. Empirical results on Tatoeba, United Nations, and BUCC show the effectiveness of our lightweight models. We release our lightweight language-agnostic sentence embedding models LEALLA on TensorFlow Hub.",
+}
+```

config.json

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+  "initializer_range": 0.02,
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+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 24,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.29.2",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 501153
+}

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+}