Sentence Similarity
sentence-transformers
Safetensors
English
modernbert
biencoder
text-classification
sentence-pair-classification
semantic-similarity
semantic-search
retrieval
reranking
Generated from Trainer
dataset_size:483820
loss:MultipleNegativesSymmetricRankingLoss
Eval Results
text-embeddings-inference
Add new SentenceTransformer model
Browse files- README.md +94 -90
- config.json +1 -1
- model.safetensors +2 -2
README.md
CHANGED
@@ -16,50 +16,54 @@ tags:
|
|
16 |
- loss:CoSENTLoss
|
17 |
base_model: Alibaba-NLP/gte-modernbert-base
|
18 |
widget:
|
19 |
-
- source_sentence:
|
20 |
-
|
21 |
sentences:
|
22 |
-
-
|
23 |
-
|
24 |
-
-
|
25 |
-
|
26 |
-
-
|
27 |
-
|
28 |
-
- source_sentence:
|
29 |
-
|
30 |
sentences:
|
31 |
-
-
|
32 |
-
|
33 |
-
-
|
34 |
-
|
35 |
-
-
|
36 |
-
|
37 |
-
|
38 |
-
|
|
|
39 |
sentences:
|
40 |
-
-
|
41 |
-
|
42 |
-
-
|
43 |
-
|
44 |
-
|
45 |
-
|
46 |
-
|
|
|
47 |
sentences:
|
48 |
-
-
|
49 |
-
|
50 |
-
|
51 |
-
|
52 |
-
|
53 |
-
|
54 |
-
|
55 |
-
|
56 |
sentences:
|
57 |
-
-
|
58 |
-
|
59 |
-
|
60 |
-
|
61 |
-
|
62 |
-
|
|
|
|
|
63 |
datasets:
|
64 |
- redis/langcache-sentencepairs-v1
|
65 |
pipeline_tag: sentence-similarity
|
@@ -84,28 +88,28 @@ model-index:
|
|
84 |
type: val
|
85 |
metrics:
|
86 |
- type: cosine_accuracy
|
87 |
-
value: 0.
|
88 |
name: Cosine Accuracy
|
89 |
- type: cosine_accuracy_threshold
|
90 |
-
value: 0.
|
91 |
name: Cosine Accuracy Threshold
|
92 |
- type: cosine_f1
|
93 |
-
value: 0.
|
94 |
name: Cosine F1
|
95 |
- type: cosine_f1_threshold
|
96 |
-
value: 0.
|
97 |
name: Cosine F1 Threshold
|
98 |
- type: cosine_precision
|
99 |
-
value: 0.
|
100 |
name: Cosine Precision
|
101 |
- type: cosine_recall
|
102 |
-
value: 0.
|
103 |
name: Cosine Recall
|
104 |
- type: cosine_ap
|
105 |
-
value: 0.
|
106 |
name: Cosine Ap
|
107 |
- type: cosine_mcc
|
108 |
-
value: 0.
|
109 |
name: Cosine Mcc
|
110 |
- task:
|
111 |
type: binary-classification
|
@@ -115,28 +119,28 @@ model-index:
|
|
115 |
type: test
|
116 |
metrics:
|
117 |
- type: cosine_accuracy
|
118 |
-
value: 0.
|
119 |
name: Cosine Accuracy
|
120 |
- type: cosine_accuracy_threshold
|
121 |
-
value: 0.
|
122 |
name: Cosine Accuracy Threshold
|
123 |
- type: cosine_f1
|
124 |
-
value: 0.
|
125 |
name: Cosine F1
|
126 |
- type: cosine_f1_threshold
|
127 |
-
value: 0.
|
128 |
name: Cosine F1 Threshold
|
129 |
- type: cosine_precision
|
130 |
-
value: 0.
|
131 |
name: Cosine Precision
|
132 |
- type: cosine_recall
|
133 |
-
value: 0.
|
134 |
name: Cosine Recall
|
135 |
- type: cosine_ap
|
136 |
-
value: 0.
|
137 |
name: Cosine Ap
|
138 |
- type: cosine_mcc
|
139 |
-
value: 0.
|
140 |
name: Cosine Mcc
|
141 |
---
|
142 |
|
@@ -190,9 +194,9 @@ from sentence_transformers import SentenceTransformer
|
|
190 |
model = SentenceTransformer("redis/langcache-embed-v3")
|
191 |
# Run inference
|
192 |
sentences = [
|
193 |
-
'
|
194 |
-
'
|
195 |
-
'
|
196 |
]
|
197 |
embeddings = model.encode(sentences)
|
198 |
print(embeddings.shape)
|
@@ -201,9 +205,9 @@ print(embeddings.shape)
|
|
201 |
# Get the similarity scores for the embeddings
|
202 |
similarities = model.similarity(embeddings, embeddings)
|
203 |
print(similarities)
|
204 |
-
# tensor([[1.
|
205 |
-
# [0.
|
206 |
-
# [0.
|
207 |
```
|
208 |
|
209 |
<!--
|
@@ -241,14 +245,14 @@ You can finetune this model on your own dataset.
|
|
241 |
|
242 |
| Metric | val | test |
|
243 |
|:--------------------------|:-----------|:-----------|
|
244 |
-
| cosine_accuracy | 0.
|
245 |
-
| cosine_accuracy_threshold | 0.8641 | 0.
|
246 |
-
| cosine_f1 | 0.
|
247 |
-
| cosine_f1_threshold | 0.
|
248 |
-
| cosine_precision | 0.6289 | 0.
|
249 |
-
| cosine_recall | 0.
|
250 |
-
| **cosine_ap** | **0.
|
251 |
-
| cosine_mcc | 0.
|
252 |
|
253 |
<!--
|
254 |
## Bias, Risks and Limitations
|
@@ -269,19 +273,19 @@ You can finetune this model on your own dataset.
|
|
269 |
#### LangCache Sentence Pairs (all)
|
270 |
|
271 |
* Dataset: [LangCache Sentence Pairs (all)](https://huggingface.co/datasets/redis/langcache-sentencepairs-v1)
|
272 |
-
* Size:
|
273 |
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
|
274 |
* Approximate statistics based on the first 1000 samples:
|
275 |
-
| | sentence1 | sentence2
|
276 |
-
|
277 |
-
| type | string | string
|
278 |
-
| details | <ul><li>min:
|
279 |
* Samples:
|
280 |
-
| sentence1
|
281 |
-
|
282 |
-
| <code>
|
283 |
-
| <code>
|
284 |
-
| <code>
|
285 |
* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
|
286 |
```json
|
287 |
{
|
@@ -295,19 +299,19 @@ You can finetune this model on your own dataset.
|
|
295 |
#### LangCache Sentence Pairs (all)
|
296 |
|
297 |
* Dataset: [LangCache Sentence Pairs (all)](https://huggingface.co/datasets/redis/langcache-sentencepairs-v1)
|
298 |
-
* Size:
|
299 |
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
|
300 |
* Approximate statistics based on the first 1000 samples:
|
301 |
-
| | sentence1 | sentence2
|
302 |
-
|
303 |
-
| type | string | string
|
304 |
-
| details | <ul><li>min:
|
305 |
* Samples:
|
306 |
-
| sentence1
|
307 |
-
|
308 |
-
| <code>
|
309 |
-
| <code>
|
310 |
-
| <code>
|
311 |
* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
|
312 |
```json
|
313 |
{
|
@@ -319,7 +323,7 @@ You can finetune this model on your own dataset.
|
|
319 |
### Training Logs
|
320 |
| Epoch | Step | val_cosine_ap | test_cosine_ap |
|
321 |
|:-----:|:----:|:-------------:|:--------------:|
|
322 |
-
| -1 | -1 | 0.
|
323 |
|
324 |
|
325 |
### Framework Versions
|
|
|
16 |
- loss:CoSENTLoss
|
17 |
base_model: Alibaba-NLP/gte-modernbert-base
|
18 |
widget:
|
19 |
+
- source_sentence: That is evident from their failure , three times in a row , to
|
20 |
+
get a big enough turnout to elect a president .
|
21 |
sentences:
|
22 |
+
- 'given a text, decide to which of a predefined set of classes it belongs. examples:
|
23 |
+
language identification, genre classification, sentiment analysis, and spam detection'
|
24 |
+
- Three times in a row , they failed to get a big _ enough turnout to elect a president
|
25 |
+
.
|
26 |
+
- He said the Government still did not know the real reason the original Saudi buyer
|
27 |
+
pulled out on August 21 .
|
28 |
+
- source_sentence: these use built-in and learned knowledge to make decisions and
|
29 |
+
accomplish tasks that fulfill the intentions of the user.
|
30 |
sentences:
|
31 |
+
- It also features a 4.5 in back-lit LCD screen and memory expansion facilities
|
32 |
+
.
|
33 |
+
- '- set of interrelated components - collect, process, store and distribute info.
|
34 |
+
- support decision-making, coordination, and control'
|
35 |
+
- software programs that work without direct human intervention to carry out specific
|
36 |
+
tasks for an individual user, business process, or software application -siri
|
37 |
+
adapts to your preferences over time
|
38 |
+
- source_sentence: any location in storage can be accessed at any moment in approximately
|
39 |
+
the same amount of time.
|
40 |
sentences:
|
41 |
+
- your study can adopt the original model used by the cited theorist but you can
|
42 |
+
modify different variables depending on your study of the whole theory
|
43 |
+
- an access method that can access any storage location directly and in any order;
|
44 |
+
primary storage devices and disk storage devices use random access...
|
45 |
+
- Branson said that his preference would be to operate a fully commercial service
|
46 |
+
on routes to New York , Barbados and Dubai .
|
47 |
+
- source_sentence: United issued a statement saying it will " work professionally
|
48 |
+
and cooperatively with all its unions . "
|
49 |
sentences:
|
50 |
+
- network that acts like the human brain; type of ai
|
51 |
+
- a database system consists of one or more databases and a database management
|
52 |
+
system (dbms).
|
53 |
+
- Senior vice president Sara Fields said the airline " will work professionally
|
54 |
+
and cooperatively with all our unions . "
|
55 |
+
- source_sentence: A European Union spokesman said the Commission was consulting EU
|
56 |
+
member states " with a view to taking appropriate action if necessary " on the
|
57 |
+
matter .
|
58 |
sentences:
|
59 |
+
- Justice Minister Martin Cauchon and Prime Minister Jean Chretien both have said
|
60 |
+
the government will introduce legislation to decriminalize possession of small
|
61 |
+
amounts of pot .
|
62 |
+
- Laos 's second most important export destination - said it was consulting EU member
|
63 |
+
states ' ' with a view to taking appropriate action if necessary ' ' on the matter
|
64 |
+
.
|
65 |
+
- the form data assumes and the possible range of values that the attribute defined
|
66 |
+
as that type of data may express 1. text 2. numerical
|
67 |
datasets:
|
68 |
- redis/langcache-sentencepairs-v1
|
69 |
pipeline_tag: sentence-similarity
|
|
|
88 |
type: val
|
89 |
metrics:
|
90 |
- type: cosine_accuracy
|
91 |
+
value: 0.7638310529446758
|
92 |
name: Cosine Accuracy
|
93 |
- type: cosine_accuracy_threshold
|
94 |
+
value: 0.8640533685684204
|
95 |
name: Cosine Accuracy Threshold
|
96 |
- type: cosine_f1
|
97 |
+
value: 0.6912742186395134
|
98 |
name: Cosine F1
|
99 |
- type: cosine_f1_threshold
|
100 |
+
value: 0.825770378112793
|
101 |
name: Cosine F1 Threshold
|
102 |
- type: cosine_precision
|
103 |
+
value: 0.6289243437982501
|
104 |
name: Cosine Precision
|
105 |
- type: cosine_recall
|
106 |
+
value: 0.7673469387755102
|
107 |
name: Cosine Recall
|
108 |
- type: cosine_ap
|
109 |
+
value: 0.7353968345121902
|
110 |
name: Cosine Ap
|
111 |
- type: cosine_mcc
|
112 |
+
value: 0.4778469995044085
|
113 |
name: Cosine Mcc
|
114 |
- task:
|
115 |
type: binary-classification
|
|
|
119 |
type: test
|
120 |
metrics:
|
121 |
- type: cosine_accuracy
|
122 |
+
value: 0.7037777526966672
|
123 |
name: Cosine Accuracy
|
124 |
- type: cosine_accuracy_threshold
|
125 |
+
value: 0.8524033427238464
|
126 |
name: Cosine Accuracy Threshold
|
127 |
- type: cosine_f1
|
128 |
+
value: 0.7122170715871171
|
129 |
name: Cosine F1
|
130 |
- type: cosine_f1_threshold
|
131 |
+
value: 0.8118724822998047
|
132 |
name: Cosine F1 Threshold
|
133 |
- type: cosine_precision
|
134 |
+
value: 0.5989283084033827
|
135 |
name: Cosine Precision
|
136 |
- type: cosine_recall
|
137 |
+
value: 0.8783612662942272
|
138 |
name: Cosine Recall
|
139 |
- type: cosine_ap
|
140 |
+
value: 0.6476665223951498
|
141 |
name: Cosine Ap
|
142 |
- type: cosine_mcc
|
143 |
+
value: 0.44182914870985407
|
144 |
name: Cosine Mcc
|
145 |
---
|
146 |
|
|
|
194 |
model = SentenceTransformer("redis/langcache-embed-v3")
|
195 |
# Run inference
|
196 |
sentences = [
|
197 |
+
'A European Union spokesman said the Commission was consulting EU member states " with a view to taking appropriate action if necessary " on the matter .',
|
198 |
+
"Laos 's second most important export destination - said it was consulting EU member states ' ' with a view to taking appropriate action if necessary ' ' on the matter .",
|
199 |
+
'the form data assumes and the possible range of values that the attribute defined as that type of data may express 1. text 2. numerical',
|
200 |
]
|
201 |
embeddings = model.encode(sentences)
|
202 |
print(embeddings.shape)
|
|
|
205 |
# Get the similarity scores for the embeddings
|
206 |
similarities = model.similarity(embeddings, embeddings)
|
207 |
print(similarities)
|
208 |
+
# tensor([[1.0078, 0.8789, 0.4961],
|
209 |
+
# [0.8789, 1.0000, 0.4648],
|
210 |
+
# [0.4961, 0.4648, 1.0078]], dtype=torch.bfloat16)
|
211 |
```
|
212 |
|
213 |
<!--
|
|
|
245 |
|
246 |
| Metric | val | test |
|
247 |
|:--------------------------|:-----------|:-----------|
|
248 |
+
| cosine_accuracy | 0.7638 | 0.7038 |
|
249 |
+
| cosine_accuracy_threshold | 0.8641 | 0.8524 |
|
250 |
+
| cosine_f1 | 0.6913 | 0.7122 |
|
251 |
+
| cosine_f1_threshold | 0.8258 | 0.8119 |
|
252 |
+
| cosine_precision | 0.6289 | 0.5989 |
|
253 |
+
| cosine_recall | 0.7673 | 0.8784 |
|
254 |
+
| **cosine_ap** | **0.7354** | **0.6477** |
|
255 |
+
| cosine_mcc | 0.4778 | 0.4418 |
|
256 |
|
257 |
<!--
|
258 |
## Bias, Risks and Limitations
|
|
|
273 |
#### LangCache Sentence Pairs (all)
|
274 |
|
275 |
* Dataset: [LangCache Sentence Pairs (all)](https://huggingface.co/datasets/redis/langcache-sentencepairs-v1)
|
276 |
+
* Size: 8,405 training samples
|
277 |
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
|
278 |
* Approximate statistics based on the first 1000 samples:
|
279 |
+
| | sentence1 | sentence2 | label |
|
280 |
+
|:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:------------------------------------------------|
|
281 |
+
| type | string | string | int |
|
282 |
+
| details | <ul><li>min: 6 tokens</li><li>mean: 24.89 tokens</li><li>max: 50 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 24.3 tokens</li><li>max: 43 tokens</li></ul> | <ul><li>0: ~45.80%</li><li>1: ~54.20%</li></ul> |
|
283 |
* Samples:
|
284 |
+
| sentence1 | sentence2 | label |
|
285 |
+
|:--------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------|:---------------|
|
286 |
+
| <code>He said the foodservice pie business doesn 't fit the company 's long-term growth strategy .</code> | <code>" The foodservice pie business does not fit our long-term growth strategy .</code> | <code>1</code> |
|
287 |
+
| <code>Magnarelli said Racicot hated the Iraqi regime and looked forward to using his long years of training in the war .</code> | <code>His wife said he was " 100 percent behind George Bush " and looked forward to using his years of training in the war .</code> | <code>0</code> |
|
288 |
+
| <code>The dollar was at 116.92 yen against the yen , flat on the session , and at 1.2891 against the Swiss franc , also flat .</code> | <code>The dollar was at 116.78 yen JPY = , virtually flat on the session , and at 1.2871 against the Swiss franc CHF = , down 0.1 percent .</code> | <code>0</code> |
|
289 |
* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
|
290 |
```json
|
291 |
{
|
|
|
299 |
#### LangCache Sentence Pairs (all)
|
300 |
|
301 |
* Dataset: [LangCache Sentence Pairs (all)](https://huggingface.co/datasets/redis/langcache-sentencepairs-v1)
|
302 |
+
* Size: 8,405 evaluation samples
|
303 |
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
|
304 |
* Approximate statistics based on the first 1000 samples:
|
305 |
+
| | sentence1 | sentence2 | label |
|
306 |
+
|:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:------------------------------------------------|
|
307 |
+
| type | string | string | int |
|
308 |
+
| details | <ul><li>min: 6 tokens</li><li>mean: 24.89 tokens</li><li>max: 50 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 24.3 tokens</li><li>max: 43 tokens</li></ul> | <ul><li>0: ~45.80%</li><li>1: ~54.20%</li></ul> |
|
309 |
* Samples:
|
310 |
+
| sentence1 | sentence2 | label |
|
311 |
+
|:--------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------|:---------------|
|
312 |
+
| <code>He said the foodservice pie business doesn 't fit the company 's long-term growth strategy .</code> | <code>" The foodservice pie business does not fit our long-term growth strategy .</code> | <code>1</code> |
|
313 |
+
| <code>Magnarelli said Racicot hated the Iraqi regime and looked forward to using his long years of training in the war .</code> | <code>His wife said he was " 100 percent behind George Bush " and looked forward to using his years of training in the war .</code> | <code>0</code> |
|
314 |
+
| <code>The dollar was at 116.92 yen against the yen , flat on the session , and at 1.2891 against the Swiss franc , also flat .</code> | <code>The dollar was at 116.78 yen JPY = , virtually flat on the session , and at 1.2871 against the Swiss franc CHF = , down 0.1 percent .</code> | <code>0</code> |
|
315 |
* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
|
316 |
```json
|
317 |
{
|
|
|
323 |
### Training Logs
|
324 |
| Epoch | Step | val_cosine_ap | test_cosine_ap |
|
325 |
|:-----:|:----:|:-------------:|:--------------:|
|
326 |
+
| -1 | -1 | 0.7354 | 0.6477 |
|
327 |
|
328 |
|
329 |
### Framework Versions
|
config.json
CHANGED
@@ -12,7 +12,7 @@
|
|
12 |
"cls_token_id": 50281,
|
13 |
"decoder_bias": true,
|
14 |
"deterministic_flash_attn": false,
|
15 |
-
"dtype": "
|
16 |
"embedding_dropout": 0.0,
|
17 |
"eos_token_id": 50282,
|
18 |
"global_attn_every_n_layers": 3,
|
|
|
12 |
"cls_token_id": 50281,
|
13 |
"decoder_bias": true,
|
14 |
"deterministic_flash_attn": false,
|
15 |
+
"dtype": "bfloat16",
|
16 |
"embedding_dropout": 0.0,
|
17 |
"eos_token_id": 50282,
|
18 |
"global_attn_every_n_layers": 3,
|
model.safetensors
CHANGED
@@ -1,3 +1,3 @@
|
|
1 |
version https://git-lfs.github.com/spec/v1
|
2 |
-
oid sha256:
|
3 |
-
size
|
|
|
1 |
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:95d02211c4cca89113f9f3e93ed91f5176bf50170faa2cb835f7bfea15bb9dd2
|
3 |
+
size 298041696
|