File size: 7,269 Bytes
da80e73
 
 
 
 
 
 
0cf799f
 
ce37bcc
 
 
 
 
 
 
da80e73
ce37bcc
 
 
 
 
 
 
 
 
 
 
 
 
da80e73
 
0cf799f
 
 
 
716619a
0cf799f
 
da80e73
febfd4e
 
 
 
 
 
 
 
 
418e2b5
febfd4e
219e066
 
 
4764d2a
219e066
 
 
 
 
 
 
3b7d9ce
219e066
 
3b7d9ce
219e066
 
 
 
 
 
 
 
 
 
 
 
 
 
4764d2a
da80e73
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
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

A MahaBERT model (l3cube-pune/marathi-bert-v2) trained on the NLI 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 capabilities is shared here <a href='https://huggingface.co/l3cube-pune/indic-sentence-bert-nli'> indic-sentence-bert-nli </a> <br>

A better sentence similarity model(fine-tuned version of this model) is shared here: https://huggingface.co/l3cube-pune/marathi-sentence-similarity-sbert <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 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>

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>


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


def cls_pooling(model_output, attention_mask):
    return model_output[0][:,0]


# 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, cls pooling.
sentence_embeddings = cls_pooling(model_output, encoded_input['attention_mask'])

print("Sentence embeddings:")
print(sentence_embeddings)
```