Add new SentenceTransformer model.
Browse files- .gitattributes +3 -0
- 1_Pooling/config.json +7 -0
- README.md +125 -0
- config.json +26 -0
- config_sentence_transformers.json +7 -0
- eval/similarity_evaluation_results.csv +61 -0
- modules.json +14 -0
- pytorch_model.bin +3 -0
- sentence_bert_config.json +4 -0
- similarity_evaluation_sts-test_results.csv +2 -0
- special_tokens_map.json +1 -0
- tokenizer.json +3 -0
- tokenizer_config.json +1 -0
- unigram.json +3 -0
.gitattributes
CHANGED
@@ -25,3 +25,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
|
|
25 |
*.zip filter=lfs diff=lfs merge=lfs -text
|
26 |
*.zstandard filter=lfs diff=lfs merge=lfs -text
|
27 |
*tfevents* filter=lfs diff=lfs merge=lfs -text
|
|
|
|
|
|
|
|
25 |
*.zip filter=lfs diff=lfs merge=lfs -text
|
26 |
*.zstandard filter=lfs diff=lfs merge=lfs -text
|
27 |
*tfevents* filter=lfs diff=lfs merge=lfs -text
|
28 |
+
unigram.json filter=lfs diff=lfs merge=lfs -text
|
29 |
+
tokenizer.json filter=lfs diff=lfs merge=lfs -text
|
30 |
+
pytorch_model.bin filter=lfs diff=lfs merge=lfs -text
|
1_Pooling/config.json
ADDED
@@ -0,0 +1,7 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"word_embedding_dimension": 384,
|
3 |
+
"pooling_mode_cls_token": false,
|
4 |
+
"pooling_mode_mean_tokens": true,
|
5 |
+
"pooling_mode_max_tokens": false,
|
6 |
+
"pooling_mode_mean_sqrt_len_tokens": false
|
7 |
+
}
|
README.md
ADDED
@@ -0,0 +1,125 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
---
|
2 |
+
pipeline_tag: sentence-similarity
|
3 |
+
tags:
|
4 |
+
- sentence-transformers
|
5 |
+
- feature-extraction
|
6 |
+
- sentence-similarity
|
7 |
+
- transformers
|
8 |
+
---
|
9 |
+
|
10 |
+
# {MODEL_NAME}
|
11 |
+
|
12 |
+
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
|
13 |
+
|
14 |
+
<!--- Describe your model here -->
|
15 |
+
|
16 |
+
## Usage (Sentence-Transformers)
|
17 |
+
|
18 |
+
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
|
19 |
+
|
20 |
+
```
|
21 |
+
pip install -U sentence-transformers
|
22 |
+
```
|
23 |
+
|
24 |
+
Then you can use the model like this:
|
25 |
+
|
26 |
+
```python
|
27 |
+
from sentence_transformers import SentenceTransformer
|
28 |
+
sentences = ["This is an example sentence", "Each sentence is converted"]
|
29 |
+
|
30 |
+
model = SentenceTransformer('{MODEL_NAME}')
|
31 |
+
embeddings = model.encode(sentences)
|
32 |
+
print(embeddings)
|
33 |
+
```
|
34 |
+
|
35 |
+
|
36 |
+
|
37 |
+
## Usage (HuggingFace Transformers)
|
38 |
+
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.
|
39 |
+
|
40 |
+
```python
|
41 |
+
from transformers import AutoTokenizer, AutoModel
|
42 |
+
import torch
|
43 |
+
|
44 |
+
|
45 |
+
#Mean Pooling - Take attention mask into account for correct averaging
|
46 |
+
def mean_pooling(model_output, attention_mask):
|
47 |
+
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
|
48 |
+
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
|
49 |
+
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
|
50 |
+
|
51 |
+
|
52 |
+
# Sentences we want sentence embeddings for
|
53 |
+
sentences = ['This is an example sentence', 'Each sentence is converted']
|
54 |
+
|
55 |
+
# Load model from HuggingFace Hub
|
56 |
+
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
|
57 |
+
model = AutoModel.from_pretrained('{MODEL_NAME}')
|
58 |
+
|
59 |
+
# Tokenize sentences
|
60 |
+
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
|
61 |
+
|
62 |
+
# Compute token embeddings
|
63 |
+
with torch.no_grad():
|
64 |
+
model_output = model(**encoded_input)
|
65 |
+
|
66 |
+
# Perform pooling. In this case, mean pooling.
|
67 |
+
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
|
68 |
+
|
69 |
+
print("Sentence embeddings:")
|
70 |
+
print(sentence_embeddings)
|
71 |
+
```
|
72 |
+
|
73 |
+
|
74 |
+
|
75 |
+
## Evaluation Results
|
76 |
+
|
77 |
+
<!--- Describe how your model was evaluated -->
|
78 |
+
|
79 |
+
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
|
80 |
+
|
81 |
+
|
82 |
+
## Training
|
83 |
+
The model was trained with the parameters:
|
84 |
+
|
85 |
+
**DataLoader**:
|
86 |
+
|
87 |
+
`torch.utils.data.dataloader.DataLoader` of length 11 with parameters:
|
88 |
+
```
|
89 |
+
{'batch_size': 15, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
|
90 |
+
```
|
91 |
+
|
92 |
+
**Loss**:
|
93 |
+
|
94 |
+
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
|
95 |
+
|
96 |
+
Parameters of the fit()-Method:
|
97 |
+
```
|
98 |
+
{
|
99 |
+
"epochs": 5,
|
100 |
+
"evaluation_steps": 1,
|
101 |
+
"evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
|
102 |
+
"max_grad_norm": 1,
|
103 |
+
"optimizer_class": "<class 'transformers.optimization.AdamW'>",
|
104 |
+
"optimizer_params": {
|
105 |
+
"lr": 2e-05
|
106 |
+
},
|
107 |
+
"scheduler": "WarmupLinear",
|
108 |
+
"steps_per_epoch": null,
|
109 |
+
"warmup_steps": 6,
|
110 |
+
"weight_decay": 0.01
|
111 |
+
}
|
112 |
+
```
|
113 |
+
|
114 |
+
|
115 |
+
## Full Model Architecture
|
116 |
+
```
|
117 |
+
SentenceTransformer(
|
118 |
+
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel
|
119 |
+
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
|
120 |
+
)
|
121 |
+
```
|
122 |
+
|
123 |
+
## Citing & Authors
|
124 |
+
|
125 |
+
<!--- Describe where people can find more information -->
|
config.json
ADDED
@@ -0,0 +1,26 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"_name_or_path": "/root/.cache/torch/sentence_transformers/sentence-transformers_paraphrase-multilingual-MiniLM-L12-v2/",
|
3 |
+
"architectures": [
|
4 |
+
"BertModel"
|
5 |
+
],
|
6 |
+
"attention_probs_dropout_prob": 0.1,
|
7 |
+
"classifier_dropout": null,
|
8 |
+
"gradient_checkpointing": false,
|
9 |
+
"hidden_act": "gelu",
|
10 |
+
"hidden_dropout_prob": 0.1,
|
11 |
+
"hidden_size": 384,
|
12 |
+
"initializer_range": 0.02,
|
13 |
+
"intermediate_size": 1536,
|
14 |
+
"layer_norm_eps": 1e-12,
|
15 |
+
"max_position_embeddings": 512,
|
16 |
+
"model_type": "bert",
|
17 |
+
"num_attention_heads": 12,
|
18 |
+
"num_hidden_layers": 12,
|
19 |
+
"pad_token_id": 0,
|
20 |
+
"position_embedding_type": "absolute",
|
21 |
+
"torch_dtype": "float32",
|
22 |
+
"transformers_version": "4.16.2",
|
23 |
+
"type_vocab_size": 2,
|
24 |
+
"use_cache": true,
|
25 |
+
"vocab_size": 250037
|
26 |
+
}
|
config_sentence_transformers.json
ADDED
@@ -0,0 +1,7 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"__version__": {
|
3 |
+
"sentence_transformers": "2.0.0",
|
4 |
+
"transformers": "4.7.0",
|
5 |
+
"pytorch": "1.9.0+cu102"
|
6 |
+
}
|
7 |
+
}
|
eval/similarity_evaluation_results.csv
ADDED
@@ -0,0 +1,61 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
epoch,steps,cosine_pearson,cosine_spearman,euclidean_pearson,euclidean_spearman,manhattan_pearson,manhattan_spearman,dot_pearson,dot_spearman
|
2 |
+
0,1,0.4106261751588814,0.41069823848644194,0.39567371234645116,0.4539296320113305,0.37736095233478156,0.4081552153379191,-0.05937455447312464,-0.24667324540671745
|
3 |
+
0,2,0.41703347562800147,0.4310424236746248,0.39495681024825563,0.4208703310805334,0.3762328507432742,0.4081552153379191,-0.06870090518429162,-0.24667324540671745
|
4 |
+
0,3,0.43387628146379564,0.43867149312019343,0.3949820838999695,0.4157842847834876,0.37694433412458683,0.44248602784297775,-0.08389158302407942,-0.18818371299069164
|
5 |
+
0,4,0.43888751276830884,0.464101724605422,0.3887693374722494,0.3827249838526905,0.37054486403862874,0.3789104491299062,-0.11465797345590431,-0.24794475698097887
|
6 |
+
0,5,0.4199516289483206,0.46283021303116056,0.3718819775042325,0.3216924282881418,0.3541219756219773,0.31787789356535756,-0.14057270119398318,-0.31279184726831183
|
7 |
+
0,6,0.3636268475717739,0.3077058009712661,0.34532352176771713,0.3038912662484818,0.33009164119396317,0.3077058009712661,-0.1652263206336318,-0.3598377755159847
|
8 |
+
0,7,0.27058883954378427,0.20979940975313596,0.3103824614293719,0.20852789817887454,0.2975129443163477,0.19708429401052166,-0.1828569049848596,-0.3598377755159847
|
9 |
+
0,8,0.17460599159763063,0.16021045835694017,0.2685757982240059,0.1983558055847831,0.25744516029998266,0.2021703403075674,-0.1915139522620683,-0.3700098681100762
|
10 |
+
0,9,0.10970843059534678,0.11952208798057443,0.23127618295899868,0.17038255095103164,0.22184607060968276,0.1856406898421688,-0.19929718098431048,-0.41196975006070335
|
11 |
+
0,10,0.06884684690475125,0.10934999538648299,0.20040156742091517,0.11570755325779014,0.1912876656806486,0.1309656921489273,-0.20212439030800322,-0.39544009959530474
|
12 |
+
0,11,0.044849195434396555,-0.043231393524888626,0.17480423437686426,0.10553546066369869,0.16406036472244478,0.14622383104006448,-0.20547530014127693,-0.39544009959530474
|
13 |
+
0,-1,0.044849195434396555,-0.043231393524888626,0.17480423437686426,0.10553546066369869,0.16406036472244478,0.14622383104006448,-0.20547530014127693,-0.39544009959530474
|
14 |
+
1,1,0.029028796048977516,-0.10299243751517584,0.1570090393795128,0.0928203449210844,0.1450816296615038,0.10426394908943727,-0.2051709607706394,-0.3344075440307561
|
15 |
+
1,2,0.02522248337174138,-0.10172092594091442,0.15824742456568783,0.08773429862403868,0.1462905290999444,0.11062150696074444,-0.19799103462794115,-0.32042091671388034
|
16 |
+
1,3,0.017323894964116293,-0.09790639121813012,0.15130458176520872,0.10680697223796014,0.13883722516764563,0.08010522917847009,-0.1940989069538219,-0.28990463893160606
|
17 |
+
1,4,0.003297061631681285,-0.12587964585188158,0.1313143042287291,0.054674997693241495,0.1165413177039431,0.04450290509915005,-0.19626476790808867,-0.3115203356940504
|
18 |
+
1,5,0.00726098197234125,-0.1691110393767702,0.14313992188027375,0.026701743059490034,0.12869884400675458,0.01907267361392145,-0.18788543710798475,-0.23777266438688743
|
19 |
+
1,6,0.005664336541190259,-0.18818371299069164,0.14301861112598646,0.026701743059490034,0.1274927498404123,0.01907267361392145,-0.18614394327566008,-0.20852789817887454
|
20 |
+
1,7,-0.010660180532377495,-0.2530308032780246,0.11533163030981536,-0.021615696762444313,0.09784505807015652,0.01907267361392145,-0.19564072131600976,-0.2492162685552403
|
21 |
+
1,8,-0.01715041047089929,-0.2822755694860375,0.09701273196824367,-0.01398662731687573,0.07545848808051656,-0.02924476620801289,-0.20485300893866354,-0.26447440744637746
|
22 |
+
1,9,-0.02038561512442355,-0.24158719910967172,0.07967078254411952,-0.021615696762444313,0.049409196526083984,-0.02924476620801289,-0.21363332115422054,-0.28609010420882175
|
23 |
+
1,10,-0.008418116334679829,-0.16275348150546307,0.08363694769312821,-0.005086046297045721,0.041821647184158456,-0.0673901134358558,-0.21830085187329173,-0.3077058009712661
|
24 |
+
1,11,0.015586916032080874,-0.16656801622824735,0.09798356020185679,0.021615696762444313,0.040982966744801515,-0.03051627778227432,-0.2219992374306971,-0.33059300930797186
|
25 |
+
1,-1,0.015586916032080874,-0.16656801622824735,0.09798356020185679,0.021615696762444313,0.040982966744801515,-0.03051627778227432,-0.2219992374306971,-0.33059300930797186
|
26 |
+
2,1,0.047801516019719714,0.00762906944556858,0.11980525772875769,0.06484709028733295,0.054392079338261866,-0.0025430231485228604,-0.22284381969988437,-0.33059300930797186
|
27 |
+
2,2,0.07956714760005196,0.012715115742614302,0.13888356320107245,0.12333662270335873,0.06957069998087369,-0.0025430231485228604,-0.22176210730918058,-0.33059300930797186
|
28 |
+
2,3,0.1057183312921956,0.1474953426143259,0.15260537096939022,0.17546859724807737,0.0827703181283367,0.04704592824767291,-0.21855764869145566,-0.30261975467422036
|
29 |
+
2,4,0.12934688896830013,0.2352296412383646,0.1657997690056382,0.19962731715904453,0.09756826904484978,0.06993313658437866,-0.21132281263687777,-0.34839417134763184
|
30 |
+
2,5,0.14782488778792863,0.2822755694860375,0.17727911754805137,0.18436917826790739,0.11069177402183757,0.08519127547551582,-0.2014845070966012,-0.3560232407932004
|
31 |
+
2,6,0.15637574668593676,0.2568453380008089,0.1829389204809058,0.21361394447592028,0.11754178862428834,0.09536336806960725,-0.19538267690560565,-0.38653951857547475
|
32 |
+
2,7,0.157137225006789,0.2568453380008089,0.18359359844830836,0.15512441205989447,0.11841104894438723,0.08519127547551582,-0.19500228522297441,-0.38653951857547475
|
33 |
+
2,8,0.15265641320433912,0.1907267361392145,0.1809694568252187,0.13350871529745015,0.11561229136264951,0.08519127547551582,-0.19616413846992908,-0.38653951857547475
|
34 |
+
2,9,0.1559446697666977,0.1691110393767702,0.18254506061649572,0.13350871529745015,0.11686882559570261,0.06611860186159436,-0.19269131224899028,-0.38653951857547475
|
35 |
+
2,10,0.1598883770901154,0.16148196993120162,0.1845205349414064,0.13350871529745015,0.11872648508716209,0.04450290509915005,-0.19025755081059587,-0.3763674259813833
|
36 |
+
2,11,0.16335588368015394,0.18182615511938452,0.185548428788402,0.12333662270335873,0.11869123710973699,0.04450290509915005,-0.1891281323598976,-0.4043406806151348
|
37 |
+
2,-1,0.16335588368015394,0.18182615511938452,0.185548428788402,0.12333662270335873,0.11869123710973699,0.04450290509915005,-0.1891281323598976,-0.4043406806151348
|
38 |
+
3,1,0.16084878565735056,0.15639592363415591,0.18291369778515698,0.12333662270335873,0.1153313199190561,0.0025430231485228604,-0.1897234269863271,-0.37763893755564476
|
39 |
+
3,2,0.15987176101415468,0.12079359955483586,0.18160800507886074,0.13986627316875733,0.11372207329898108,0.0025430231485228604,-0.18903305293982103,-0.3700098681100762
|
40 |
+
3,3,0.1591909281264029,0.12079359955483586,0.18068883949077896,0.13986627316875733,0.11262448158692233,-0.04450290509915005,-0.188623826709613,-0.3700098681100762
|
41 |
+
3,4,0.15694779200303524,0.10044941436665299,0.17944668209481687,0.08519127547551582,0.11295697539978297,-0.0419598819506272,-0.1894529219316317,-0.3700098681100762
|
42 |
+
3,5,0.15511632246525442,0.10299243751517584,0.17894555764704256,0.0928203449210844,0.11444465471685551,-0.0419598819506272,-0.18932334844160703,-0.33695056717927896
|
43 |
+
3,6,0.15542689574399937,0.10299243751517584,0.17938798800482234,0.06866162501011723,0.11570420028785418,-0.0419598819506272,-0.1872997840516068,-0.33695056717927896
|
44 |
+
3,7,0.15546340531113298,0.10044941436665299,0.18055153549236613,0.06866162501011723,0.11818501847813453,0.03560232407932004,-0.1857944636835592,-0.33695056717927896
|
45 |
+
3,8,0.16214808746316528,0.12587964585188158,0.18609684289616127,0.04958895139619578,0.12601642554151612,0.04958895139619578,-0.18399996951169115,-0.3115203356940504
|
46 |
+
3,9,0.17254063491690202,0.10044941436665299,0.1944187664636575,0.07120464815864008,0.13785884705189178,0.03941685880210433,-0.18150021551845624,-0.32550696301092613
|
47 |
+
3,10,0.17638997854462649,0.11825057640631301,0.19872060123012816,0.13350871529745015,0.14506844102241745,0.03941685880210433,-0.18011112216071898,-0.32550696301092613
|
48 |
+
3,11,0.18449115393072982,0.11825057640631301,0.20709479718510213,0.1296941805746659,0.15575158593730679,0.05086046297045721,-0.17795467761013317,-0.31024882411978894
|
49 |
+
3,-1,0.18449115393072982,0.11825057640631301,0.20709479718510213,0.1296941805746659,0.15575158593730679,0.05086046297045721,-0.17795467761013317,-0.31024882411978894
|
50 |
+
4,1,0.19014118310036487,0.11825057640631301,0.2134476561852836,0.1296941805746659,0.1634979170414422,0.09663487964386869,-0.17629394695817932,-0.31024882411978894
|
51 |
+
4,2,0.19321773662151137,0.11825057640631301,0.2178857749959978,0.1296941805746659,0.1687328738866751,0.08264825232699297,-0.17491056873445324,-0.31024882411978894
|
52 |
+
4,3,0.19636698312816747,0.11825057640631301,0.22060934997798368,0.1296941805746659,0.17140318738455818,0.04704592824767291,-0.17374133367999142,-0.30261975467422036
|
53 |
+
4,4,0.19748606882909112,0.10044941436665299,0.22151561837191408,0.10426394908943727,0.1718291033396474,0.06103255556454864,-0.17308408081355006,-0.30261975467422036
|
54 |
+
4,5,0.19940729493487955,0.04958895139619578,0.22251960921676367,0.10426394908943727,0.17228917664172388,0.06103255556454864,-0.17233135741857689,-0.30261975467422036
|
55 |
+
4,6,0.20165697846038638,0.031787789356535756,0.22386873711347385,0.10426394908943727,0.1731868970810523,0.06103255556454864,-0.171558745760406,-0.30261975467422036
|
56 |
+
4,7,0.20405371256404706,0.012715115742614302,0.2253802677892442,0.10426394908943727,0.17445471467868595,0.06103255556454864,-0.17083077224245694,-0.30261975467422036
|
57 |
+
4,8,0.20528716666399116,0.012715115742614302,0.2263630661975477,0.08264825232699297,0.17542231230551567,0.06103255556454864,-0.17037942684347893,-0.30261975467422036
|
58 |
+
4,9,0.20612393783010774,0.012715115742614302,0.22752355864670962,0.07756220602994723,0.17686262312518253,0.06103255556454864,-0.1699562582446593,-0.30261975467422036
|
59 |
+
4,10,0.20709131114742294,0.012715115742614302,0.22886365829774544,0.07756220602994723,0.17861490130210808,0.06103255556454864,-0.16953728319760075,-0.30261975467422036
|
60 |
+
4,11,0.20708357118430498,0.012715115742614302,0.2293017214864497,0.07756220602994723,0.17930350756603547,0.06103255556454864,-0.16939245137472528,-0.30261975467422036
|
61 |
+
4,-1,0.20708357118430498,0.012715115742614302,0.2293017214864497,0.07756220602994723,0.17930350756603547,0.06103255556454864,-0.16939245137472528,-0.30261975467422036
|
modules.json
ADDED
@@ -0,0 +1,14 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
[
|
2 |
+
{
|
3 |
+
"idx": 0,
|
4 |
+
"name": "0",
|
5 |
+
"path": "",
|
6 |
+
"type": "sentence_transformers.models.Transformer"
|
7 |
+
},
|
8 |
+
{
|
9 |
+
"idx": 1,
|
10 |
+
"name": "1",
|
11 |
+
"path": "1_Pooling",
|
12 |
+
"type": "sentence_transformers.models.Pooling"
|
13 |
+
}
|
14 |
+
]
|
pytorch_model.bin
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:10799b50d3bb3b960eb291e7c6b0e3a8bb74bf87deec42733c712ff636c90d8b
|
3 |
+
size 470696369
|
sentence_bert_config.json
ADDED
@@ -0,0 +1,4 @@
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"max_seq_length": 128,
|
3 |
+
"do_lower_case": false
|
4 |
+
}
|
similarity_evaluation_sts-test_results.csv
ADDED
@@ -0,0 +1,2 @@
|
|
|
|
|
|
|
1 |
+
epoch,steps,cosine_pearson,cosine_spearman,euclidean_pearson,euclidean_spearman,manhattan_pearson,manhattan_spearman,dot_pearson,dot_spearman
|
2 |
+
-1,-1,0.6610049361985745,0.14846384769101478,0.5905155340577791,0.1257945101768986,0.5102097495363672,0.1212281418317612,0.6183753965624046,0.3497740649848295
|
special_tokens_map.json
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
{"bos_token": "<s>", "eos_token": "</s>", "unk_token": "<unk>", "sep_token": "</s>", "pad_token": "<pad>", "cls_token": "<s>", "mask_token": {"content": "<mask>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": false}}
|
tokenizer.json
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:3a3313815c3d2e1b78b5182b09e66e6cd4cdd54df67a35c4a318c23d461821a4
|
3 |
+
size 17082913
|
tokenizer_config.json
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
{"do_lower_case": true, "unk_token": "<unk>", "sep_token": "</s>", "pad_token": "<pad>", "cls_token": "<s>", "mask_token": {"content": "<mask>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "tokenize_chinese_chars": true, "strip_accents": null, "bos_token": "<s>", "eos_token": "</s>", "model_max_length": 512, "special_tokens_map_file": null, "name_or_path": "/root/.cache/torch/sentence_transformers/sentence-transformers_paraphrase-multilingual-MiniLM-L12-v2/", "tokenizer_class": "BertTokenizer"}
|
unigram.json
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:71b44701d7efd054205115acfa6ef126c5d2f84bd3affe0c59e48163674d19a6
|
3 |
+
size 14763234
|