to be pure model

License.txt

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-MIT License
-
-Copyright © 2020 nnnmu24
-
-Permission is hereby granted, free of charge, to any person obtaining a copy
-of this software and associated documentation files (the "Software"), to deal
-in the Software without restriction, including without limitation the rights
-to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-copies of the Software, and to permit persons to whom the Software is
-furnished to do so, subject to the following conditions:
-
-The above copyright notice and this permission notice shall be included in all
-copies or substantial portions of the Software.
-
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-SOFTWARE.

README.md

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-# Model: 
-converted from the below model.
-https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2
-
-# License
-This project is under the MIT License except for the Model.

index.d.ts

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-declare module 'embedding';
-declare async function embedding_calc(text: string): Promise<number[]>;
-export { embedding_calc }

index.js

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-import path from 'path';
-import { fileURLToPath } from 'url';
-import { env, pipeline } from "@xenova/transformers";
-
-const __filename = fileURLToPath(import.meta.url);
-const __dirname = path.dirname(__filename);
-env.localModelPath = __dirname + '/model'
-
-let pipe = null;
-const piping = pipeline("feature-extraction", "sentence-transformers/paraphrase-multilingual-mpnet-base-v2", { local_files_only: true, quantized: true }).then(p => { pipe = p; })
-
-export async function embedding_calc(text) {
-    await piping;
-    if (pipe) {
-        return [...(await pipe(text)).data.values()];
-    }
-}

model/sentence-transformers/paraphrase-multilingual-mpnet-base-v2/README.md

@@ -1,163 +0,0 @@
----
-language:
-- multilingual
-- ar
-- bg
-- ca
-- cs
-- da
-- de
-- el
-- en 
-- es 
-- et 
-- fa 
-- fi 
-- fr
-- gl 
-- gu
-- he 
-- hi
-- hr
-- hu 
-- hy
-- id
-- it
-- ja
-- ka
-- ko
-- ku
-- lt
-- lv
-- mk
-- mn
-- mr
-- ms
-- my
-- nb
-- nl
-- pl
-- pt
-- ro
-- ru
-- sk
-- sl 
-- sq 
-- sr 
-- sv
-- th
-- tr
-- uk
-- ur
-- vi
-language_bcp47:
-- fr-ca
-- pt-br 
-- zh-cn
-- zh-tw
-pipeline_tag: sentence-similarity
-license: apache-2.0
-tags:
-- sentence-transformers
-- feature-extraction
-- sentence-similarity
-- transformers
----
-
-# sentence-transformers/paraphrase-multilingual-mpnet-base-v2
-
-This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
-
-
-
-## Usage (Sentence-Transformers)
-
-Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
-
-```
-pip install -U sentence-transformers
-```
-
-Then you can use the model like this:
-
-```python
-from sentence_transformers import SentenceTransformer
-sentences = ["This is an example sentence", "Each sentence is converted"]
-
-model = SentenceTransformer('sentence-transformers/paraphrase-multilingual-mpnet-base-v2')
-embeddings = model.encode(sentences)
-print(embeddings)
-```
-
-
-
-## Usage (HuggingFace Transformers)
-Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
-
-```python
-from transformers import AutoTokenizer, AutoModel
-import torch
-
-
-#Mean Pooling - Take attention mask into account for correct averaging
-def mean_pooling(model_output, attention_mask):
-    token_embeddings = model_output[0] #First element of model_output contains all token embeddings
-    input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
-    return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
-
-
-# Sentences we want sentence embeddings for
-sentences = ['This is an example sentence', 'Each sentence is converted']
-
-# Load model from HuggingFace Hub
-tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/paraphrase-multilingual-mpnet-base-v2')
-model = AutoModel.from_pretrained('sentence-transformers/paraphrase-multilingual-mpnet-base-v2')
-
-# Tokenize sentences
-encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
-
-# Compute token embeddings
-with torch.no_grad():
-    model_output = model(**encoded_input)
-
-# Perform pooling. In this case, average pooling
-sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
-
-print("Sentence embeddings:")
-print(sentence_embeddings)
-```
-
-
-
-## Evaluation Results
-
-
-
-For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=sentence-transformers/paraphrase-multilingual-mpnet-base-v2)
-
-
-
-## Full Model Architecture
-```
-SentenceTransformer(
-  (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: XLMRobertaModel 
-  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
-)
-```
-
-## Citing & Authors
-
-This model was trained by [sentence-transformers](https://www.sbert.net/). 
-        
-If you find this model helpful, feel free to cite our publication [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://arxiv.org/abs/1908.10084):
-```bibtex 
-@inproceedings{reimers-2019-sentence-bert,
-    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
-    author = "Reimers, Nils and Gurevych, Iryna",
-    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
-    month = "11",
-    year = "2019",
-    publisher = "Association for Computational Linguistics",
-    url = "http://arxiv.org/abs/1908.10084",
-}
-```