Add model card, re-export model

README.md

@@ -1,3 +1,223 @@
 ---
+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
+
+## 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-large).
+- Paper: [arXiv](https://arxiv.org/abs/2302.08387).
+- Original model: [TensorFlow Hub](https://tfhub.dev/google/LEALLA/LEALLA-large/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-large")
+model = BertModel.from_pretrained("setu4993/LEALLA-large")
+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
+@misc{mao2023lealla,
+      title={LEALLA: Learning Lightweight Language-agnostic Sentence Embeddings with Knowledge Distillation},
+      author={Zhuoyuan Mao and Tetsuji Nakagawa},
+      year={2023},
+      eprint={2302.08387},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+```

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