Hugging Face's logo

ShauryaNova
/
autotrain-tuac9-vfsuc

Sentence Similarity
sentence-transformers
TensorBoard
Safetensors
bert
feature-extraction
Trained with AutoTrain
text-embeddings-inference
Model card Files Metrics Community
autotrain-tuac9-vfsuc
File size: 1,797 Bytes 
6db602e
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
 

---
library_name: sentence-transformers
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- autotrain
base_model: sentence-transformers/all-MiniLM-L6-v2
widget:
- source_sentence: 'search_query: i love autotrain'
  sentences:
  - 'search_query: huggingface auto train'
  - 'search_query: hugging face auto train'
  - 'search_query: i love autotrain'
pipeline_tag: sentence-similarity
---

# Model Trained Using AutoTrain

- Problem type: Sentence Transformers

## Validation Metrics
loss: 9.164422988891602

validation_pearson_cosine: -0.10073561135203735

validation_spearman_cosine: -0.05129891760425771

validation_pearson_manhattan: -0.07223520049199797

validation_spearman_manhattan: -0.05129891760425771

validation_pearson_euclidean: -0.056592337170460805

validation_spearman_euclidean: -0.05129891760425771

validation_pearson_dot: -0.1007351930231386

validation_spearman_dot: -0.05129891760425771

validation_pearson_max: -0.056592337170460805

validation_spearman_max: -0.05129891760425771

runtime: 0.1267

samples_per_second: 39.454

steps_per_second: 7.891

: 3.0

## Usage

### Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

```bash
pip install -U sentence-transformers
```

Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer

# Download from the Hugging Face Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
    'search_query: autotrain',
    'search_query: auto train',
    'search_query: i love autotrain',
]
embeddings = model.encode(sentences)
print(embeddings.shape)

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
```