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BlackKakapo
/
cupidon-base-ro

Sentence Similarity
sentence-transformers
ONNX
Safetensors
OpenVINO
Transformers
Romanian
xlm-roberta
feature-extraction
text-embeddings-inference
Model card Files
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Community
BlackKakapo commited on
Commit
95af541
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1 Parent(s): 74e4097

Add new SentenceTransformer model with an onnx backend

Browse files
Files changed (9) hide show
  1. 1_Pooling/config.json +9 -9
  2. README.md +77 -77
  3. config.json +27 -28
  4. config_sentence_transformers.json +13 -13
  5. modules.json +13 -13
  6. onnx/model.onnx +2 -2
  7. sentence_bert_config.json +3 -3
  8. special_tokens_map.json +51 -51
  9. tokenizer_config.json +64 -64
1_Pooling/config.json CHANGED
@@ -1,10 +1,10 @@
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- {
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- "word_embedding_dimension": 768,
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- "pooling_mode_cls_token": false,
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- "pooling_mode_mean_tokens": true,
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- "pooling_mode_weightedmean_tokens": false,
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- "pooling_mode_lasttoken": false,
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- "include_prompt": true
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  }
 
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+ {
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+ "word_embedding_dimension": 768,
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+ "pooling_mode_mean_tokens": true,
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+ "pooling_mode_max_tokens": false,
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+ "pooling_mode_weightedmean_tokens": false,
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+ "pooling_mode_lasttoken": false,
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+ "include_prompt": true
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  }
README.md CHANGED
@@ -1,78 +1,78 @@
1
- ---
2
- pipeline_tag: sentence-similarity
3
- tags:
4
- - sentence-transformers
5
- - feature-extraction
6
- - sentence-similarity
7
- - transformers
8
- language:
9
- - ro
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- language_creators:
11
- - machine-generated
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- dataset:
13
- - ro_sts
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- license: apache-2.0
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- datasets:
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- - BlackKakapo/RoSTSC
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- base_model:
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- - BlackKakapo/stsb-xlm-r-multilingual-ro
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- ---
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-
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- # 🔥 cupidon-base-ro
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-
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- Don’t be shy — cupidon-base-ro is here to charm your embeddings into alignment 💘. Based on the solid foundations of `BlackKakapo/stsb-xlm-r-multilingual-ro` and lovingly fine-tuned on Romanian STS data, this model brings more than just good looks to the table.
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- Because in the end, true meaning isn’t just in the words... it's in how you embed them. 🧠💕
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- ## Usage (Sentence-Transformers)
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-
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- Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
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-
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- ```bash
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- pip install -U sentence-transformers
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- ```
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-
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- Then you can use the model like this:
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-
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- ```python
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- from sentence_transformers import SentenceTransformer
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- sentences = ["This is an example sentence", "Each sentence is converted"]
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-
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- model = SentenceTransformer('BlackKakapo/cupidon-base-ro')
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- embeddings = model.encode(sentences)
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- print(embeddings)
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- ```
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-
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- ## Usage (HuggingFace Transformers)
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- 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.
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-
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- ```python
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- from transformers import AutoTokenizer, AutoModel
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- import torch
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-
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-
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- #Mean Pooling - Take attention mask into account for correct averaging
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- def mean_pooling(model_output, attention_mask):
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- token_embeddings = model_output[0] #First element of model_output contains all token embeddings
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- input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
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- return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
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-
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-
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- # Sentences we want sentence embeddings for
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- sentences = ['This is an example sentence', 'Each sentence is converted']
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-
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- # Load model from HuggingFace Hub
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- tokenizer = AutoTokenizer.from_pretrained('BlackKakapo/cupidon-base-ro')
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- model = AutoModel.from_pretrained('BlackKakapo/cupidon-base-ro')
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- ```
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-
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- ## License
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- This dataset is licensed under **Apache 2.0**.
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-
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- ## Citation
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- If you use BlackKakapo/cupidon-tiny-ro in your research, please cite this model as follows:
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- ```
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- @misc{cupidon-base-ro,
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- title={BlackKakapo/cupidon-base-ro},
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- author={BlackKakapo},
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- year={2025},
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- }
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  ```
 
1
+ ---
2
+ pipeline_tag: sentence-similarity
3
+ tags:
4
+ - sentence-transformers
5
+ - feature-extraction
6
+ - sentence-similarity
7
+ - transformers
8
+ language:
9
+ - ro
10
+ language_creators:
11
+ - machine-generated
12
+ dataset:
13
+ - ro_sts
14
+ license: apache-2.0
15
+ datasets:
16
+ - BlackKakapo/RoSTSC
17
+ base_model:
18
+ - BlackKakapo/stsb-xlm-r-multilingual-ro
19
+ ---
20
+
21
+ # 🔥 cupidon-base-ro
22
+
23
+ Don’t be shy — cupidon-base-ro is here to charm your embeddings into alignment 💘. Based on the solid foundations of `BlackKakapo/stsb-xlm-r-multilingual-ro` and lovingly fine-tuned on Romanian STS data, this model brings more than just good looks to the table.
24
+ Because in the end, true meaning isn’t just in the words... it's in how you embed them. 🧠💕
25
+ ## Usage (Sentence-Transformers)
26
+
27
+ Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
28
+
29
+ ```bash
30
+ pip install -U sentence-transformers
31
+ ```
32
+
33
+ Then you can use the model like this:
34
+
35
+ ```python
36
+ from sentence_transformers import SentenceTransformer
37
+ sentences = ["This is an example sentence", "Each sentence is converted"]
38
+
39
+ model = SentenceTransformer('BlackKakapo/cupidon-base-ro')
40
+ embeddings = model.encode(sentences)
41
+ print(embeddings)
42
+ ```
43
+
44
+ ## Usage (HuggingFace Transformers)
45
+ 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.
46
+
47
+ ```python
48
+ from transformers import AutoTokenizer, AutoModel
49
+ import torch
50
+
51
+
52
+ #Mean Pooling - Take attention mask into account for correct averaging
53
+ def mean_pooling(model_output, attention_mask):
54
+ token_embeddings = model_output[0] #First element of model_output contains all token embeddings
55
+ input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
56
+ return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
57
+
58
+
59
+ # Sentences we want sentence embeddings for
60
+ sentences = ['This is an example sentence', 'Each sentence is converted']
61
+
62
+ # Load model from HuggingFace Hub
63
+ tokenizer = AutoTokenizer.from_pretrained('BlackKakapo/cupidon-base-ro')
64
+ model = AutoModel.from_pretrained('BlackKakapo/cupidon-base-ro')
65
+ ```
66
+
67
+ ## License
68
+ This dataset is licensed under **Apache 2.0**.
69
+
70
+ ## Citation
71
+ If you use BlackKakapo/cupidon-tiny-ro in your research, please cite this model as follows:
72
+ ```
73
+ @misc{cupidon-base-ro,
74
+ title={BlackKakapo/cupidon-base-ro},
75
+ author={BlackKakapo},
76
+ year={2025},
77
+ }
78
  ```
config.json CHANGED
@@ -1,28 +1,27 @@
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- "architectures": [
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- "intermediate_size": 3072,
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- "model_type": "xlm-roberta",
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- "type_vocab_size": 1,
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- "use_cache": true,
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- "vocab_size": 250002
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- }
 
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+ }
 
config_sentence_transformers.json CHANGED
@@ -1,14 +1,14 @@
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- {
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- "default_prompt_name": null,
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- "similarity_fn_name": "cosine",
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- "model_type": "SentenceTransformer"
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  }
 
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  ]
 
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