ricardo-filho
commited on
Commit
•
51f9d65
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Parent(s):
a8c16e2
Add model files
Browse files- .gitattributes +1 -0
- 1_Pooling/config.json +7 -0
- Information-Retrieval_evaluation_results.csv +2 -0
- README.md +140 -0
- binary_classification_evaluation_results.csv +2 -0
- config.json +32 -0
- config_sentence_transformers.json +7 -0
- eval/Information-Retrieval_evaluation_results.csv +11 -0
- eval/binary_classification_evaluation_results.csv +11 -0
- modules.json +14 -0
- pytorch_model.bin +3 -0
- sentence_bert_config.json +4 -0
- special_tokens_map.json +1 -0
- tokenizer.json +0 -0
- tokenizer_config.json +1 -0
- vocab.txt +0 -0
.gitattributes
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*.pt filter=lfs diff=lfs merge=lfs -text
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*.pth filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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*.pt filter=lfs diff=lfs merge=lfs -text
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*.pth filter=lfs diff=lfs merge=lfs -text
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*tfevents* 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": 1024,
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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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Information-Retrieval_evaluation_results.csv
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epoch,steps,cos_sim-Accuracy@1,cos_sim-Accuracy@3,cos_sim-Accuracy@5,cos_sim-Accuracy@10,cos_sim-Precision@1,cos_sim-Recall@1,cos_sim-Precision@3,cos_sim-Recall@3,cos_sim-Precision@5,cos_sim-Recall@5,cos_sim-Precision@10,cos_sim-Recall@10,cos_sim-MRR@10,cos_sim-NDCG@10,cos_sim-MAP@100,dot_score-Accuracy@1,dot_score-Accuracy@3,dot_score-Accuracy@5,dot_score-Accuracy@10,dot_score-Precision@1,dot_score-Recall@1,dot_score-Precision@3,dot_score-Recall@3,dot_score-Precision@5,dot_score-Recall@5,dot_score-Precision@10,dot_score-Recall@10,dot_score-MRR@10,dot_score-NDCG@10,dot_score-MAP@100
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0,0,0.3128,0.4164,0.4616,0.5176,0.3128,0.2672966390489929,0.1634,0.3782775406898593,0.11412,0.42495886842224934,0.06762,0.48353173859055526,0.37572515873015777,0.3919623205740099,0.3630484803818997,0.3034,0.4064,0.4538,0.5094,0.3034,0.2588563215886755,0.15893333333333332,0.3694953423796409,0.11163999999999999,0.4169721070202526,0.06634000000000001,0.4751339454637089,0.3665266666666658,0.3830245437707761,0.3541882175668069
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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 1024 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, max 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 8605 with parameters:
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```
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{'batch_size': 24, '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.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
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```
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{'scale': 20.0, 'similarity_fct': 'cos_sim'}
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```
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**DataLoader**:
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`torch.utils.data.dataloader.DataLoader` of length 11553 with parameters:
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```
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{'batch_size': 24, '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.OnlineContrastiveLoss.OnlineContrastiveLoss`
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Parameters of the fit()-Method:
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```
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{
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"callback": null,
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"epochs": 10,
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"evaluation_steps": 0,
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"evaluator": "sentence_transformers.evaluation.SequentialEvaluator.SequentialEvaluator",
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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": 1000,
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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': 64, 'do_lower_case': False}) with Transformer model: BertModel
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(1): Pooling({'word_embedding_dimension': 1024, '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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binary_classification_evaluation_results.csv
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epoch,steps,cossim_accuracy,cossim_accuracy_threshold,cossim_f1,cossim_precision,cossim_recall,cossim_f1_threshold,cossim_ap,manhatten_accuracy,manhatten_accuracy_threshold,manhatten_f1,manhatten_precision,manhatten_recall,manhatten_f1_threshold,manhatten_ap,euclidean_accuracy,euclidean_accuracy_threshold,euclidean_f1,euclidean_precision,euclidean_recall,euclidean_f1_threshold,euclidean_ap,dot_accuracy,dot_accuracy_threshold,dot_f1,dot_precision,dot_recall,dot_f1_threshold,dot_ap
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0,0,0.7663033188174515,0.7817635536193848,0.6900327167892196,0.5946292987941045,0.8218998379504592,0.683746874332428,0.7225498136184649,0.768196694706662,359.72906494140625,0.6923865251743859,0.605668016194332,0.808087043753376,428.70892333984375,0.7241154007058747,0.7683589840685943,14.508670806884766,0.692413431956536,0.6011935208866155,0.8162666872443861,16.94521713256836,0.7242282190366673,0.756079089015715,365.9010009765625,0.6829963149908648,0.5703941243405399,0.8509915888571649,301.97802734375,0.697492958874548
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config.json
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{
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"_name_or_path": "/root/.cache/torch/sentence_transformers/ricardo-filho_sbertimbau-large-nli-sts/",
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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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"directionality": "bidi",
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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": 1024,
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"initializer_range": 0.02,
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"intermediate_size": 4096,
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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": 16,
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"num_hidden_layers": 24,
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"output_past": true,
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"pad_token_id": 0,
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"pooler_fc_size": 768,
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"pooler_num_attention_heads": 12,
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"pooler_num_fc_layers": 3,
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"pooler_size_per_head": 128,
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"pooler_type": "first_token_transform",
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"position_embedding_type": "absolute",
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"torch_dtype": "float32",
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"transformers_version": "4.9.2",
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"type_vocab_size": 2,
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"use_cache": true,
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"vocab_size": 29794
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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": "4.9.2",
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"pytorch": "1.9.0+cu102"
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}
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}
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eval/Information-Retrieval_evaluation_results.csv
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epoch,steps,cos_sim-Accuracy@1,cos_sim-Accuracy@3,cos_sim-Accuracy@5,cos_sim-Accuracy@10,cos_sim-Precision@1,cos_sim-Recall@1,cos_sim-Precision@3,cos_sim-Recall@3,cos_sim-Precision@5,cos_sim-Recall@5,cos_sim-Precision@10,cos_sim-Recall@10,cos_sim-MRR@10,cos_sim-NDCG@10,cos_sim-MAP@100,dot_score-Accuracy@1,dot_score-Accuracy@3,dot_score-Accuracy@5,dot_score-Accuracy@10,dot_score-Precision@1,dot_score-Recall@1,dot_score-Precision@3,dot_score-Recall@3,dot_score-Precision@5,dot_score-Recall@5,dot_score-Precision@10,dot_score-Recall@10,dot_score-MRR@10,dot_score-NDCG@10,dot_score-MAP@100
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0,-1,0.3042,0.4086,0.4524,0.5068,0.3042,0.26052046509422144,0.15953333333333333,0.3722873326231016,0.11232,0.4191301430394744,0.0675,0.4777220794308881,0.36769730158730063,0.38631007998336364,0.35810327380039925,0.299,0.3988,0.4426,0.5,0.299,0.2549144106954039,0.1555333333333333,0.3622523633144605,0.10987999999999999,0.41003203845960035,0.0661,0.47030703872298674,0.36038365079365015,0.3782124182844478,0.35038023801629886
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1,-1,0.2546,0.3472,0.3918,0.4474,0.2546,0.21727203762903763,0.13613333333333333,0.3150845399637963,0.09664,0.3613525614966386,0.05872,0.4185114240760715,0.3132098412698407,0.3308502622415422,0.30407132667000913,0.2412,0.3402,0.3832,0.4426,0.2412,0.2061459265179265,0.13226666666666664,0.30615063131195214,0.09432,0.3522139471439911,0.057960000000000005,0.4133753736595591,0.3027883333333328,0.3220247310223247,0.2941937006627795
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2,-1,0.2504,0.3426,0.3912,0.4442,0.2504,0.2156408799445456,0.1322,0.30887940359633237,0.09623999999999999,0.35899912452821203,0.05842000000000001,0.41653776366170187,0.30952992063492013,0.32796619377318964,0.3008477671769378,0.2428,0.3378,0.3838,0.4384,0.2428,0.20738084026200587,0.13133333333333333,0.3056145854145142,0.09508000000000001,0.3531403496919372,0.05786,0.41153183085467154,0.3022605555555554,0.32141687996178797,0.29405469407948426
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3,-1,0.2452,0.3292,0.3714,0.4242,0.2452,0.21131074969474972,0.12726666666666664,0.29893997198499145,0.09072000000000001,0.3410077051962247,0.0552,0.39720711909018436,0.29890492063492013,0.31525849693248453,0.2908521853961496,0.2384,0.3282,0.3708,0.4162,0.2384,0.20463436080586078,0.1266,0.29584536175694565,0.09084,0.340276368665242,0.054279999999999995,0.38894242156430764,0.29385079365079375,0.3092251550494858,0.28551558137787536
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4,-1,0.2468,0.3424,0.3818,0.4382,0.2468,0.21246520458196774,0.133,0.31026529697189137,0.09372,0.35011538943491255,0.05714000000000001,0.40930909539318877,0.3056201587301582,0.3232514136742135,0.29729331791071967,0.24,0.3342,0.3716,0.4308,0.24,0.2054988272194928,0.12926666666666664,0.30292099904759345,0.09144000000000001,0.34133284120536433,0.0561,0.4004912528117223,0.29818317460317434,0.315417617471875,0.29014372430340535
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7,-1,0.2446,0.34,0.3838,0.439,0.2446,0.2099429791319791,0.1314,0.3068841225302813,0.09427999999999999,0.3525891003807523,0.05708,0.40913539110033426,0.304674365079365,0.32208808017630963,0.2955806835479768,0.2352,0.3346,0.3772,0.4298,0.2352,0.20154331246531246,0.1288,0.30113563812962446,0.09232,0.34527782120671,0.056139999999999995,0.4007166235383943,0.2956877777777776,0.31345524315937107,0.2871283105831677
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9,-1,0.2492,0.3462,0.3942,0.4476,0.2492,0.21306735175051736,0.13486666666666666,0.3122877503507367,0.09688,0.36237303348170136,0.0584,0.41869156060613677,0.31005261904761894,0.3283744608043434,0.3010634978362979,0.2406,0.3402,0.3852,0.4416,0.2406,0.20549647873464433,0.1317333333333333,0.3065582752323592,0.09476000000000001,0.3536648415586818,0.0575,0.41188246041977344,0.30243103174603136,0.32101030539710235,0.2938198155210442
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eval/binary_classification_evaluation_results.csv
ADDED
@@ -0,0 +1,11 @@
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|
|
|
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|
1 |
+
epoch,steps,cossim_accuracy,cossim_accuracy_threshold,cossim_f1,cossim_precision,cossim_recall,cossim_f1_threshold,cossim_ap,manhatten_accuracy,manhatten_accuracy_threshold,manhatten_f1,manhatten_precision,manhatten_recall,manhatten_f1_threshold,manhatten_ap,euclidean_accuracy,euclidean_accuracy_threshold,euclidean_f1,euclidean_precision,euclidean_recall,euclidean_f1_threshold,euclidean_ap,dot_accuracy,dot_accuracy_threshold,dot_f1,dot_precision,dot_recall,dot_f1_threshold,dot_ap
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0,-1,0.8237537529414947,0.8060605525970459,0.7585262196621124,0.6961062403300671,0.8332433058106335,0.7445327639579773,0.8126554078667191,0.8240783316653594,356.0945739746094,0.7575610768911343,0.6867432084823389,0.8446639401188363,403.0364990234375,0.8121922486419967,0.8241324281193367,13.873517036437988,0.7580170743955807,0.7082244118238488,0.8153406898680454,15.185152053833008,0.8121722363073225,0.8151253685320927,373.408203125,0.7499829549328425,0.6717557251908397,0.8488309283123698,343.041748046875,0.7961446903345559
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5,-1,0.8285953855724757,0.774193525314331,0.7619956861497118,0.7032371753034852,0.8314684775059804,0.7179145812988281,0.8213195909434162,0.8279732763517351,370.26788330078125,0.7613954672778203,0.7202339986235375,0.8075468786171772,407.84722900390625,0.8201482106757185,0.8281626139406562,14.437774658203125,0.7613846040298178,0.729613126812363,0.796049077860946,15.77624225616455,0.8202899666398555,0.822076762868194,371.0068054199219,0.7549719619657971,0.6880396140172677,0.8363299637317694,341.44073486328125,0.8094523565474532
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10 |
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8,-1,0.8270536366341187,0.7665568590164185,0.7585083272990586,0.714480594775254,0.8083185430974612,0.7259411215782166,0.8200837445336188,0.8269995401801412,379.61676025390625,0.758544731045264,0.7029037993020346,0.8237518327031407,430.3404846191406,0.8197914505616243,0.8272970706770171,14.899150848388672,0.7586450496273534,0.6995179781136008,0.8286904853769581,16.97948455810547,0.8199256261398159,0.8213194125125098,388.5447998046875,0.7509275114155253,0.6982684269886552,0.8121768654988811,357.0634765625,0.8085797679056845
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11 |
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modules.json
ADDED
@@ -0,0 +1,14 @@
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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 |
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"idx": 0,
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4 |
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"name": "0",
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"path": "",
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6 |
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"type": "sentence_transformers.models.Transformer"
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7 |
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},
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{
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9 |
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"idx": 1,
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10 |
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"name": "1",
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11 |
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"path": "1_Pooling",
|
12 |
+
"type": "sentence_transformers.models.Pooling"
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13 |
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}
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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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oid sha256:2e51b43a57fadd928a0c008ca5a608c8d72aec3947afefc69e2b4e7a23ecb4cf
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3 |
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size 1337742513
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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": 64,
|
3 |
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"do_lower_case": false
|
4 |
+
}
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special_tokens_map.json
ADDED
@@ -0,0 +1 @@
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|
|
|
|
1 |
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{"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"}
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tokenizer.json
ADDED
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tokenizer_config.json
ADDED
@@ -0,0 +1 @@
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|
|
|
|
1 |
+
{"do_lower_case": false, "unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]", "tokenize_chinese_chars": true, "strip_accents": null, "special_tokens_map_file": "/root/.cache/huggingface/transformers/d5b721c156180bbbcc4a1017e8c72a18f8f96cdc178acec5ddcd45905712b4cf.dd8bd9bfd3664b530ea4e645105f557769387b3da9f79bdb55ed556bdd80611d", "name_or_path": "/root/.cache/torch/sentence_transformers/ricardo-filho_sbertimbau-large-nli-sts/", "do_basic_tokenize": true, "never_split": null, "tokenizer_class": "BertTokenizer"}
|
vocab.txt
ADDED
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