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README.md
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license: mit
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---
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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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license: mit
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pipeline_tag: feature-extraction
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---
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[xlm-roberta-base](https://huggingface.co/xlm-roberta-base) fine-tuned with [SimCSE](http://dx.doi.org/10.18653/v1/2021.emnlp-main.552) (Gao et al., EMNLP 2021).
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See a similar English model released by Gao et al.: https://huggingface.co/princeton-nlp/unsup-simcse-roberta-base
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Fine-tuning was done using the [reference implementation of SimCSE](https://github.com/princeton-nlp/SimCSE) and the 1M sentences from English Wikipedia released by the authors:
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```bash
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python train.py \
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--model_name_or_path xlm-roberta-base \
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--train_file data/wiki1m_for_simcse.txt \
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--output_dir unsup-simcse-xlm-roberta-base \
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--num_train_epochs 1 \
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--per_device_train_batch_size 32 \
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--gradient_accumulation_steps 16 \
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--learning_rate 1e-5 \
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--max_seq_length 128 \
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--pooler_type avg \
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--overwrite_output_dir \
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--temp 0.05 \
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--do_train \
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--fp16 \
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--seed 28852
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```
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