allow flax

.gitattributes

@@ -6,3 +6,4 @@
 *.tar.gz filter=lfs diff=lfs merge=lfs -text
 *.ot filter=lfs diff=lfs merge=lfs -text
 *.onnx filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text

LICENSE

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README.md

@@ -1,218 +0,0 @@
----
-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
-license: Apache-2.0
-datasets:
-  - CommonCrawl
-  - Wikipedia
----
-
-# LaBSE
-
-## Model description
-
-Language-agnostic BERT Sentence Encoder (LaBSE) is a BERT-based model trained for sentence embedding for 109 languages. The pre-training process combines masked language modeling with translation language modeling. The model is useful for getting multilingual sentence embeddings and for bi-text retrieval.
-
-- Model: [HuggingFace's model hub](https://huggingface.co/setu4993/LaBSE).
-- Paper: [arXiv](https://arxiv.org/abs/2007.01852).
-- Original model: [TensorFlow Hub](https://tfhub.dev/google/LaBSE/1).
-- Blog post: [Google AI Blog](https://ai.googleblog.com/2020/08/language-agnostic-bert-sentence.html).
-
-## Usage
-
-Using the model:
-
-```python
-import torch
-from transformers import BertModel, BertTokenizerFast
-
-
-tokenizer = BertTokenizerFast.from_pretrained("setu4993/LaBSE")
-model = BertModel.from_pretrained("setu4993/LaBSE")
-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/2007.01852).
-
-### BibTeX entry and citation info
-
-```bibtex
-@misc{feng2020languageagnostic,
-      title={Language-agnostic BERT Sentence Embedding},
-      author={Fangxiaoyu Feng and Yinfei Yang and Daniel Cer and Naveen Arivazhagan and Wei Wang},
-      year={2020},
-      eprint={2007.01852},
-      archivePrefix={arXiv},
-      primaryClass={cs.CL}
-}
-```

config.json

@@ -1,21 +0,0 @@
-{
-  "architectures": [
-    "BertModel"
-  ],
-  "attention_probs_dropout_prob": 0.1,
-  "gradient_checkpointing": false,
-  "hidden_act": "gelu",
-  "hidden_dropout_prob": 0.1,
-  "hidden_size": 768,
-  "initializer_range": 0.02,
-  "intermediate_size": 3072,
-  "layer_norm_eps": 1e-12,
-  "max_position_embeddings": 512,
-  "model_type": "bert",
-  "num_attention_heads": 12,
-  "num_hidden_layers": 12,
-  "pad_token_id": 0,
-  "position_embedding_type": "absolute",
-  "type_vocab_size": 2,
-  "vocab_size": 501153
-}

pytorch_model.bin

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special_tokens_map.json

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-{
-  "unk_token": "[UNK]",
-  "sep_token": "[SEP]",
-  "pad_token": "[PAD]",
-  "cls_token": "[CLS]",
-  "mask_token": "[MASK]"
-}

tf_model.h5

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tokenizer_config.json

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-{
-  "do_lower_case": false,
-  "unk_token": "[UNK]",
-  "sep_token": "[SEP]",
-  "pad_token": "[PAD]",
-  "cls_token": "[CLS]",
-  "mask_token": "[MASK]",
-  "tokenize_chinese_chars": true,
-  "strip_accents": null,
-  "do_basic_tokenize": true,
-  "never_split": null,
-  "model_max_length": 512
-}