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--- |
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language: |
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- multilingual |
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- af |
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- am |
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- ar |
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- as |
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- az |
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- be |
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- bg |
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- bn |
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- br |
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- bs |
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- ca |
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- cs |
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- cy |
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- da |
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- de |
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- el |
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- en |
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- eo |
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- es |
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- et |
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- eu |
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- fa |
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- fi |
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- fr |
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- fy |
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- ga |
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- gd |
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- gl |
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- gu |
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- ha |
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- he |
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- hi |
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- hr |
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- hu |
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- hy |
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- id |
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- is |
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- it |
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- ja |
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- jv |
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- ka |
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- kk |
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- km |
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- kn |
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- ko |
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- ku |
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- ky |
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- la |
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- lo |
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- lt |
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- lv |
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- mg |
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- mk |
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- ml |
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- mn |
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- mr |
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- ms |
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- my |
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- ne |
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- nl |
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- 'no' |
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- om |
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- or |
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- pa |
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- pl |
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- ps |
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- pt |
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- ro |
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- ru |
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- sa |
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- sd |
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- si |
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- sk |
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- sl |
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- so |
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- sq |
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- sr |
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- su |
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- sv |
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- sw |
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- ta |
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- te |
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- th |
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- tl |
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- tr |
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- ug |
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- uk |
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- ur |
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- uz |
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- vi |
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- xh |
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- yi |
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- zh |
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pipeline_tag: sentence-similarity |
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--- |
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|
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# E5-base-multilingual-4096 |
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|
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[Local-Sparse-Global](https://arxiv.org/abs/2210.15497) version of [intfloat/multilingual-e5-base](https://huggingface.co/intfloat/multilingual-e5-base). It can handle up to 4k tokens. |
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|
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### Usage |
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Below is an example to encode queries and passages from the MS-MARCO passage ranking dataset. |
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```python |
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import torch.nn.functional as F |
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from torch import Tensor |
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from transformers import AutoTokenizer, AutoModel |
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|
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def average_pool( |
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last_hidden_states: Tensor, |
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attention_mask: Tensor |
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) -> Tensor: |
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last_hidden = last_hidden_states.masked_fill(~attention_mask[..., None].bool(), 0.0) |
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return last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None] |
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|
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input_texts = [ |
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'query: how much protein should a female eat', |
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'query: summit define', |
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"passage: As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.", |
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"passage: Definition of summit for English Language Learners. : 1 the highest point of a mountain : the top of a mountain. : 2 the highest level. : 3 a meeting or series of meetings between the leaders of two or more governments." |
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] |
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tokenizer = AutoTokenizer.from_pretrained('efederici/e5-base-multilingual-4096') |
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model = AutoModel.from_pretrained('efederici/e5-base-multilingual-4096', trust_remote_code=True) |
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batch_dict = tokenizer(input_texts, max_length=4096, padding=True, truncation=True, return_tensors='pt') |
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outputs = model(**batch_dict) |
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embeddings = average_pool(outputs.last_hidden_state, batch_dict['attention_mask']) |
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|
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# (Optionally) normalize embeddings |
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embeddings = F.normalize(embeddings, p=2, dim=1) |
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scores = (embeddings[:2] @ embeddings[2:].T) * 100 |
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|
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print(scores.tolist()) |
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``` |
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|
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``` |
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@article{wang2022text, |
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title={Text Embeddings by Weakly-Supervised Contrastive Pre-training}, |
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author={Wang, Liang and Yang, Nan and Huang, Xiaolong and Jiao, Binxing and Yang, Linjun and Jiang, Daxin and Majumder, Rangan and Wei, Furu}, |
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journal={arXiv preprint arXiv:2212.03533}, |
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year={2022} |
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} |
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``` |