初始化项目，由ModelHub XC社区提供模型
Model: l3cube-pune/marathi-sentence-similarity-sbert Source: Original Platform
This commit is contained in:
ModelHub XC
140
README.md
Normal file
140
README.md
Normal file
@@ -0,0 +1,140 @@
|
||||
---
|
||||
pipeline_tag: sentence-similarity
|
||||
tags:
|
||||
- sentence-transformers
|
||||
- feature-extraction
|
||||
- sentence-similarity
|
||||
- transformers
|
||||
base_model: l3cube-pune/marathi-sentence-bert-nli
|
||||
license: cc-by-4.0
|
||||
language: mr
|
||||
widget:
|
||||
- source_sentence: "शेतकऱ्यांचे डोळे आकाशाकडे लागले आहेत"
|
||||
sentences:
|
||||
- "आता शेतकऱ्यांचे डोळे आभाळाकडे लागले आहेत"
|
||||
- "अन्नधान्य उत्पादनासाठी शेतकरी कष्ट करतात"
|
||||
- "शहरात कचऱ्याचे ढीग दिसतात"
|
||||
example_title: "Example 1"
|
||||
|
||||
- source_sentence: "घटनेची माहिती मिळताच पोलिसांचा ताफा तेथे पोहोचला"
|
||||
sentences:
|
||||
- "पोलिसांना घटनेची माहिती मिळताच त्यांचे पथक घटनास्थळी पोहोचले"
|
||||
- "तेव्हा पोलिसांनी त्यांच्या तक्रारीची दखल घेतली नाही"
|
||||
- "दिवसाचा उत्तरार्ध कुटुंबासोबत मौजमजेत घालवाल"
|
||||
example_title: "Example 2"
|
||||
|
||||
- source_sentence: "पहिल्या पाच किलोमीटर अंतरासाठी पाच रुपये दर आकारण्यात येत आहे"
|
||||
sentences:
|
||||
- "पाच रुपयांत पाच किमी प्रवास करा"
|
||||
- "दोन ठिकाणांमधले मोठे अंतर प्रवास करणे कंटाळवाणे आहे"
|
||||
- "नुकत्याच झालेल्या पावसामुळे हिरवळ दिसत आहे"
|
||||
example_title: "Example 3"
|
||||
---
|
||||
|
||||
# MahaSBERT-STS
|
||||
|
||||
A MahaSBERT model (l3cube-pune/marathi-sentence-bert-nli) fine-tuned on STS dataset. <br>
|
||||
This is released as a part of project MahaNLP : https://github.com/l3cube-pune/MarathiNLP <br>
|
||||
A multilingual version of this model supporting major Indic languages and cross-lingual sentence similarity is shared here <a href='https://huggingface.co/l3cube-pune/indic-sentence-similarity-sbert'> indic-sentence-similarity-sbert </a> <br>
|
||||
|
||||
More details on the dataset, models, and baseline results can be found in our [paper] (https://arxiv.org/abs/2211.11187)
|
||||
|
||||
```
|
||||
@article{joshi2022l3cubemahasbert,
|
||||
title={L3Cube-MahaSBERT and HindSBERT: Sentence BERT Models and Benchmarking BERT Sentence Representations for Hindi and Marathi},
|
||||
author={Joshi, Ananya and Kajale, Aditi and Gadre, Janhavi and Deode, Samruddhi and Joshi, Raviraj},
|
||||
journal={arXiv preprint arXiv:2211.11187},
|
||||
year={2022}
|
||||
}
|
||||
```
|
||||
|
||||
<a href='https://arxiv.org/abs/2211.11187'> monolingual Indic SBERT paper </a> <br>
|
||||
<a href='https://arxiv.org/abs/2304.11434'> multilingual Indic SBERT paper </a>
|
||||
|
||||
Other Monolingual similarity models are listed below: <br>
|
||||
<a href='https://huggingface.co/l3cube-pune/marathi-sentence-similarity-sbert'> Marathi Similarity </a> <br>
|
||||
<a href='https://huggingface.co/l3cube-pune/hindi-sentence-similarity-sbert'> Hindi Similarity </a> <br>
|
||||
<a href='https://huggingface.co/l3cube-pune/kannada-sentence-similarity-sbert'> Kannada Similarity </a> <br>
|
||||
<a href='https://huggingface.co/l3cube-pune/telugu-sentence-similarity-sbert'> Telugu Similarity </a> <br>
|
||||
<a href='https://huggingface.co/l3cube-pune/malayalam-sentence-similarity-sbert'> Malayalam Similarity </a> <br>
|
||||
<a href='https://huggingface.co/l3cube-pune/tamil-sentence-similarity-sbert'> Tamil Similarity </a> <br>
|
||||
<a href='https://huggingface.co/l3cube-pune/gujarati-sentence-similarity-sbert'> Gujarati Similarity </a> <br>
|
||||
<a href='https://huggingface.co/l3cube-pune/odia-sentence-similarity-sbert'> Oriya Similarity </a> <br>
|
||||
<a href='https://huggingface.co/l3cube-pune/bengali-sentence-similarity-sbert'> Bengali Similarity </a> <br>
|
||||
<a href='https://huggingface.co/l3cube-pune/punjabi-sentence-similarity-sbert'> Punjabi Similarity </a> <br>
|
||||
<a href='https://huggingface.co/l3cube-pune/indic-sentence-similarity-sbert'> Indic Similarity (multilingual)</a> <br>
|
||||
|
||||
Other Monolingual Indic sentence BERT models are listed below: <br>
|
||||
<a href='https://huggingface.co/l3cube-pune/marathi-sentence-bert-nli'> Marathi SBERT</a> <br>
|
||||
<a href='https://huggingface.co/l3cube-pune/hindi-sentence-bert-nli'> Hindi SBERT</a> <br>
|
||||
<a href='https://huggingface.co/l3cube-pune/kannada-sentence-bert-nli'> Kannada SBERT</a> <br>
|
||||
<a href='https://huggingface.co/l3cube-pune/telugu-sentence-bert-nli'> Telugu SBERT</a> <br>
|
||||
<a href='https://huggingface.co/l3cube-pune/malayalam-sentence-bert-nli'> Malayalam SBERT</a> <br>
|
||||
<a href='https://huggingface.co/l3cube-pune/tamil-sentence-bert-nli'> Tamil SBERT</a> <br>
|
||||
<a href='https://huggingface.co/l3cube-pune/gujarati-sentence-bert-nli'> Gujarati SBERT</a> <br>
|
||||
<a href='https://huggingface.co/l3cube-pune/odia-sentence-bert-nli'> Oriya SBERT</a> <br>
|
||||
<a href='https://huggingface.co/l3cube-pune/bengali-sentence-bert-nli'> Bengali SBERT</a> <br>
|
||||
<a href='https://huggingface.co/l3cube-pune/punjabi-sentence-bert-nli'> Punjabi SBERT</a> <br>
|
||||
<a href='https://huggingface.co/l3cube-pune/indic-sentence-bert-nli'> Indic SBERT (multilingual)</a> <br>
|
||||
|
||||
|
||||
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.
|
||||
|
||||
<!--- Describe your model here -->
|
||||
|
||||
## 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('{MODEL_NAME}')
|
||||
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('{MODEL_NAME}')
|
||||
model = AutoModel.from_pretrained('{MODEL_NAME}')
|
||||
|
||||
# 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, mean pooling.
|
||||
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
|
||||
|
||||
print("Sentence embeddings:")
|
||||
print(sentence_embeddings)
|
||||
```
|
||||
Reference in New Issue
Block a user