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# Text Embedding with Transformers
*author: [Jael Gu](https://github.com/jaelgu)*
<br />
## Description
A text embedding operator takes a sentence, paragraph, or document in string as an input
and output an embedding vector in ndarray which captures the input's core semantic elements.
This operator is implemented with pre-trained models from [Huggingface Transformers](https://huggingface.co/docs/transformers).
<br />
## Code Example
Use the pre-trained model 'distilbert-base-cased'
to generate a text embedding for the sentence "Hello, world.".
*Write the pipeline*:
```python
import towhee
towhee.dc(["Hello, world."]) \
.text_embedding.transformers(model_name="distilbert-base-cased")
```
<br />
## Factory Constructor
Create the operator via the following factory method:
***text_embedding.transformers(model_name="bert-base-uncased")***
**Parameters:**
***model_name***: *str*
The model name in string.
The default model name is "bert-base-uncased".
Supported model names:
<details><summary>Albert</summary>
- albert-base-v1
- albert-large-v1
- albert-xlarge-v1
- albert-xxlarge-v1
- albert-base-v2
- albert-large-v2
- albert-xlarge-v2
- albert-xxlarge-v2
</details>
<details><summary>Bart</summary>
- facebook/bart-large
</details>
<details><summary>Bert</summary>
- bert-base-cased
- bert-large-cased
- bert-base-multilingual-uncased
- bert-base-multilingual-cased
- bert-base-chinese
- bert-base-german-cased
- bert-large-uncased-whole-word-masking
- bert-large-cased-whole-word-masking
- bert-large-uncased-whole-word-masking-finetuned-squad
- bert-large-cased-whole-word-masking-finetuned-squad
- bert-base-cased-finetuned-mrpc
- bert-base-german-dbmdz-cased
- bert-base-german-dbmdz-uncased
- cl-tohoku/bert-base-japanese-whole-word-masking
- cl-tohoku/bert-base-japanese-char
- cl-tohoku/bert-base-japanese-char-whole-word-masking
- TurkuNLP/bert-base-finnish-cased-v1
- TurkuNLP/bert-base-finnish-uncased-v1
- wietsedv/bert-base-dutch-cased
</details>
<details><summary>BertGeneration</summary>
- google/bert_for_seq_generation_L-24_bbc_encoder
</details>
<details><summary>BigBird</summary>
- google/bigbird-roberta-base
- google/bigbird-roberta-large
- google/bigbird-base-trivia-itc
</details>
<details><summary>BigBirdPegasus</summary>
- google/bigbird-pegasus-large-arxiv
- google/bigbird-pegasus-large-pubmed
- google/bigbird-pegasus-large-bigpatent
</details>
<details><summary>CamemBert</summary>
- camembert-base
- Musixmatch/umberto-commoncrawl-cased-v1
- Musixmatch/umberto-wikipedia-uncased-v1
</details>
<details><summary>Canine</summary>
- google/canine-s
- google/canine-c
</details>
<details><summary>Convbert</summary>
- YituTech/conv-bert-base
- YituTech/conv-bert-medium-small
- YituTech/conv-bert-small
</details>
<details><summary>CTRL</summary>
- ctrl
</details>
<details><summary>DeBERTa</summary>
- microsoft/deberta-base
- microsoft/deberta-large
- microsoft/deberta-xlarge
- microsoft/deberta-base-mnli
- microsoft/deberta-large-mnli
- microsoft/deberta-xlarge-mnli
- microsoft/deberta-v2-xlarge
- microsoft/deberta-v2-xxlarge
- microsoft/deberta-v2-xlarge-mnli
- microsoft/deberta-v2-xxlarge-mnli
</details>
<details><summary>DistilBert</summary>
- distilbert-base-uncased
- distilbert-base-uncased-distilled-squad
- distilbert-base-cased
- distilbert-base-cased-distilled-squad
- distilbert-base-german-cased
- distilbert-base-multilingual-cased
- distilbert-base-uncased-finetuned-sst-2-english
</details>
<details><summary>Electral</summary>
- google/electra-small-generator
- google/electra-base-generator
- google/electra-large-generator
- google/electra-small-discriminator
- google/electra-base-discriminator
- google/electra-large-discriminator
</details>
<details><summary>Flaubert</summary>
- flaubert/flaubert_small_cased
- flaubert/flaubert_base_uncased
- flaubert/flaubert_base_cased
- flaubert/flaubert_large_cased
</details>
<details><summary>FNet</summary>
- google/fnet-base
- google/fnet-large
</details>
<details><summary>FSMT</summary>
- facebook/wmt19-ru-en
</details>
<details><summary>Funnel</summary>
- funnel-transformer/small
- funnel-transformer/small-base
- funnel-transformer/medium
- funnel-transformer/medium-base
- funnel-transformer/intermediate
- funnel-transformer/intermediate-base
- funnel-transformer/large
- funnel-transformer/large-base
- funnel-transformer/xlarge-base
- funnel-transformer/xlarge
</details>
<details><summary>GPT</summary>
- openai-gpt
- gpt2
- gpt2-medium
- gpt2-large
- gpt2-xl
- distilgpt2
- EleutherAI/gpt-neo-1.3B
- EleutherAI/gpt-j-6B
</details>
<details><summary>I-Bert</summary>
- kssteven/ibert-roberta-base
</details>
<details><summary>LED</summary>
- allenai/led-base-16384
</details>
<details><summary>MobileBert</summary>
- google/mobilebert-uncased
</details>
<details><summary>MPNet</summary>
- microsoft/mpnet-base
</details>
<details><summary>Nystromformer</summary>
- uw-madison/nystromformer-512
</details>
<details><summary>Reformer</summary>
- google/reformer-crime-and-punishment
</details>
<details><summary>Splinter</summary>
- tau/splinter-base
- tau/splinter-base-qass
- tau/splinter-large
- tau/splinter-large-qass
</details>
<details><summary>SqueezeBert</summary>
- squeezebert/squeezebert-uncased
- squeezebert/squeezebert-mnli
- squeezebert/squeezebert-mnli-headless
</details>
<details><summary>TransfoXL</summary>
- transfo-xl-wt103
</details>
<details><summary>XLM</summary>
- xlm-mlm-en-2048
- xlm-mlm-ende-1024
- xlm-mlm-enfr-1024
- xlm-mlm-enro-1024
- xlm-mlm-tlm-xnli15-1024
- xlm-mlm-xnli15-1024
- xlm-clm-enfr-1024
- xlm-clm-ende-1024
- xlm-mlm-17-1280
- xlm-mlm-100-1280
</details>
<details><summary>XLMRoberta</summary>
- xlm-roberta-base
- xlm-roberta-large
- xlm-roberta-large-finetuned-conll02-dutch
- xlm-roberta-large-finetuned-conll02-spanish
- xlm-roberta-large-finetuned-conll03-english
- xlm-roberta-large-finetuned-conll03-german
</details>
<details><summary>XLNet</summary>
- xlnet-base-cased
- xlnet-large-cased
</details>
<details><summary>Yoso</summary>
- uw-madison/yoso-4096
</details>
<br />
## Interface
The operator takes a piece of text in string as input.
It loads tokenizer and pre-trained model using model name.
and then return text embedding in ndarray.
**Parameters:**
***text***: *str*
​ The text in string.
**Returns**:
*numpy.ndarray*
​ The text embedding extracted by model.