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 +361 -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
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@@ -25,3 +25,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zstandard filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zstandard filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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unigram.json filter=lfs diff=lfs merge=lfs -text
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tokenizer.json filter=lfs diff=lfs merge=lfs -text
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pytorch_model.bin filter=lfs diff=lfs merge=lfs -text
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1_Pooling/config.json
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{
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"word_embedding_dimension": 384,
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"pooling_mode_cls_token": false,
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"pooling_mode_mean_tokens": true,
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"pooling_mode_max_tokens": false,
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"pooling_mode_mean_sqrt_len_tokens": false
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}
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README.md
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---
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pipeline_tag: sentence-similarity
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tags:
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- sentence-transformers
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- feature-extraction
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- sentence-similarity
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- transformers
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---
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# {MODEL_NAME}
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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.
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<!--- Describe your model here -->
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## Usage (Sentence-Transformers)
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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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pip install -U sentence-transformers
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```
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Then you can use the model like this:
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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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model = SentenceTransformer('{MODEL_NAME}')
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embeddings = model.encode(sentences)
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print(embeddings)
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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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```python
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from transformers import AutoTokenizer, AutoModel
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import torch
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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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# Sentences we want sentence embeddings for
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sentences = ['This is an example sentence', 'Each sentence is converted']
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# Load model from HuggingFace Hub
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tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
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model = AutoModel.from_pretrained('{MODEL_NAME}')
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# Tokenize sentences
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encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
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# Compute token embeddings
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with torch.no_grad():
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model_output = model(**encoded_input)
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# Perform pooling. In this case, mean pooling.
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sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
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print("Sentence embeddings:")
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print(sentence_embeddings)
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```
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## Evaluation Results
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<!--- Describe how your model was evaluated -->
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For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
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## Training
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The model was trained with the parameters:
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**DataLoader**:
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`torch.utils.data.dataloader.DataLoader` of length 11 with parameters:
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```
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{'batch_size': 15, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
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```
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**Loss**:
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`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
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Parameters of the fit()-Method:
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```
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{
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"epochs": 30,
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"evaluation_steps": 1,
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"evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
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"max_grad_norm": 1,
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"optimizer_class": "<class 'transformers.optimization.AdamW'>",
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"optimizer_params": {
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"lr": 2e-05
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},
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"scheduler": "WarmupLinear",
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"steps_per_epoch": null,
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"warmup_steps": 33,
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"weight_decay": 0.01
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}
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```
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## Full Model Architecture
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```
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SentenceTransformer(
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(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel
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(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})
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)
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```
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## Citing & Authors
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<!--- Describe where people can find more information -->
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config.json
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{
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"_name_or_path": "/root/.cache/torch/sentence_transformers/sentence-transformers_paraphrase-multilingual-MiniLM-L12-v2/",
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"architectures": [
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"BertModel"
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],
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"attention_probs_dropout_prob": 0.1,
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"classifier_dropout": null,
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"gradient_checkpointing": false,
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"hidden_act": "gelu",
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"hidden_dropout_prob": 0.1,
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"hidden_size": 384,
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"initializer_range": 0.02,
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"intermediate_size": 1536,
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"layer_norm_eps": 1e-12,
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"max_position_embeddings": 512,
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"model_type": "bert",
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"num_attention_heads": 12,
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"num_hidden_layers": 12,
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"pad_token_id": 0,
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"position_embedding_type": "absolute",
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"torch_dtype": "float32",
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"transformers_version": "4.16.2",
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"type_vocab_size": 2,
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"use_cache": true,
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"vocab_size": 250037
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}
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config_sentence_transformers.json
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{
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"__version__": {
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"sentence_transformers": "2.0.0",
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"transformers": "4.7.0",
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"pytorch": "1.9.0+cu102"
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}
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}
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eval/similarity_evaluation_results.csv
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epoch,steps,cosine_pearson,cosine_spearman,euclidean_pearson,euclidean_spearman,manhattan_pearson,manhattan_spearman,dot_pearson,dot_spearman
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0,1,0.4106261751588814,0.41069823848644194,0.39567371234645116,0.4539296320113305,0.37736095233478156,0.4081552153379191,-0.05937455447312464,-0.24667324540671745
|
3 |
+
0,2,0.41399099199235495,0.4310424236746248,0.3955196664335195,0.4208703310805334,0.37709988582368925,0.4081552153379191,-0.06400785454294725,-0.24667324540671745
|
4 |
+
0,3,0.4182866209696506,0.43867149312019343,0.3941593815089011,0.3878110301497362,0.37549034375285395,0.4081552153379191,-0.07294614426254756,-0.2403156875354103
|
5 |
+
0,4,0.421860843692077,0.43867149312019343,0.39165295378494436,0.4246848658033177,0.3726980320539004,0.43739998154593196,-0.08492034811969819,-0.21869999077296598
|
6 |
+
0,5,0.4239208798834829,0.44248602784297775,0.3870519342192739,0.4590156783083763,0.36796569366760307,0.4157842847834876,-0.09642861149679856,-0.18818371299069164
|
7 |
+
0,6,0.42239410298569685,0.4081552153379191,0.38110109258391056,0.4056121921893962,0.36196757592283885,0.4157842847834876,-0.10713117342093255,-0.2199715023472274
|
8 |
+
0,7,0.4171545495718688,0.46028718988263767,0.3740998339451476,0.3916255648725205,0.35495384899934795,0.39035405329825906,-0.11741263691783375,-0.2199715023472274
|
9 |
+
0,8,0.4077596019892301,0.47173079405099055,0.36502088520177517,0.36238079866450756,0.34588591128940516,0.36238079866450756,-0.12734555241922005,-0.27591801161473034
|
10 |
+
0,9,0.3890951780064018,0.43739998154593196,0.3551422387792079,0.36238079866450756,0.3365445648777553,0.3661953333872919,-0.13321848170669445,-0.32423545143666466
|
11 |
+
0,10,0.3643657078468313,0.35093719449615474,0.3441538261500743,0.3344075440307561,0.3263187363669036,0.27973254633751465,-0.13593525584919525,-0.381453472278429
|
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29,5,0.350176821149236,0.3318645208822333,0.42391020260745527,0.4882604445163891,0.4193780434867778,0.4882604445163891,0.06009557695307842,-0.024158719910967172
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355 |
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29,6,0.35039518912970846,0.3318645208822333,0.4239335876173948,0.4882604445163891,0.41938739586089024,0.4882604445163891,0.060431474291446866,-0.024158719910967172
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356 |
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29,7,0.3505673176225451,0.3318645208822333,0.42392179949150605,0.4882604445163891,0.4193603614325398,0.4882604445163891,0.06070437599966076,-0.024158719910967172
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357 |
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29,8,0.3506950376970433,0.3318645208822333,0.4239201999053281,0.4882604445163891,0.4193486305901996,0.4882604445163891,0.060892029009333805,-0.024158719910967172
|
358 |
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29,9,0.3507537300147216,0.3318645208822333,0.4239158553688196,0.4882604445163891,0.4193366815251689,0.4882604445163891,0.06098224482938859,-0.024158719910967172
|
359 |
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29,10,0.35076725217458277,0.3318645208822333,0.4239099338828944,0.4882604445163891,0.4193267348984611,0.4882604445163891,0.061011122994839934,-0.024158719910967172
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360 |
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29,11,0.3508633458797164,0.3318645208822333,0.42395926616635027,0.4882604445163891,0.4193794208980744,0.4882604445163891,0.061074506113237635,-0.024158719910967172
|
361 |
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29,-1,0.3508633458797164,0.3318645208822333,0.42395926616635027,0.4882604445163891,0.4193794208980744,0.4882604445163891,0.061074506113237635,-0.024158719910967172
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modules.json
ADDED
@@ -0,0 +1,14 @@
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|
|
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|
|
|
|
|
|
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|
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|
|
|
|
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|
|
|
|
1 |
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[
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2 |
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{
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3 |
+
"idx": 0,
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4 |
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"name": "0",
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5 |
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"path": "",
|
6 |
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"type": "sentence_transformers.models.Transformer"
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7 |
+
},
|
8 |
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{
|
9 |
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"idx": 1,
|
10 |
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"name": "1",
|
11 |
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"path": "1_Pooling",
|
12 |
+
"type": "sentence_transformers.models.Pooling"
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13 |
+
}
|
14 |
+
]
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pytorch_model.bin
ADDED
@@ -0,0 +1,3 @@
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1 |
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version https://git-lfs.github.com/spec/v1
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2 |
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oid sha256:329bf767f512716a391511a8b27337b4239ba007162a7d3316c0018bfd52260e
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3 |
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size 470696369
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sentence_bert_config.json
ADDED
@@ -0,0 +1,4 @@
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|
1 |
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{
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2 |
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"max_seq_length": 128,
|
3 |
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"do_lower_case": false
|
4 |
+
}
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similarity_evaluation_sts-test_results.csv
ADDED
@@ -0,0 +1,2 @@
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|
|
|
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|
|
1 |
+
epoch,steps,cosine_pearson,cosine_spearman,euclidean_pearson,euclidean_spearman,manhattan_pearson,manhattan_spearman,dot_pearson,dot_spearman
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2 |
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-1,-1,0.7240146488844361,0.3238708936604559,0.6346197776033915,0.2307722308515873,0.5888050146044485,0.23173100569985455,0.5681589287074926,0.4182533397407333
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special_tokens_map.json
ADDED
@@ -0,0 +1 @@
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|
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|
1 |
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{"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}}
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tokenizer.json
ADDED
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1 |
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version https://git-lfs.github.com/spec/v1
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oid sha256:3a3313815c3d2e1b78b5182b09e66e6cd4cdd54df67a35c4a318c23d461821a4
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size 17082913
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tokenizer_config.json
ADDED
@@ -0,0 +1 @@
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|
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|
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"}
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unigram.json
ADDED
@@ -0,0 +1,3 @@
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|
1 |
+
version https://git-lfs.github.com/spec/v1
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oid sha256:71b44701d7efd054205115acfa6ef126c5d2f84bd3affe0c59e48163674d19a6
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size 14763234
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