luismsgomes
commited on
Commit
•
34c1355
1
Parent(s):
f93b612
added trained model
Browse files- 1_Pooling/config.json +10 -0
- README.md +131 -3
- config.json +32 -0
- config_sentence_transformers.json +9 -0
- eval/similarity_evaluation_assin-ptbr-test_results.csv +2 -0
- eval/similarity_evaluation_assin-ptpt-test_results.csv +2 -0
- eval/similarity_evaluation_assin2-test_results.csv +2 -0
- eval/similarity_evaluation_iris-sts-test_results.csv +2 -0
- eval/similarity_evaluation_stsb-multi-mt-pt-test_results.csv +2 -0
- eval/similarity_evaluation_validation_results.csv +201 -0
- model.safetensors +3 -0
- modules.json +14 -0
- sentence_bert_config.json +4 -0
- special_tokens_map.json +37 -0
- tokenizer.json +0 -0
- tokenizer_config.json +64 -0
- train-config.yaml +12 -0
- vocab.txt +0 -0
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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"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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README.md
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-
---
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license: mit
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---
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license: mit
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library_name: sentence-transformers
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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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# Serafim 335m Portuguese (PT) Sentence Encoder
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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, 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 253 with parameters:
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```
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{'batch_size': 75, '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.AnglELoss.AnglELoss` with parameters:
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```
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{'scale': 20.0, 'similarity_fct': 'pairwise_angle_sim'}
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```
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Parameters of the fit()-Method:
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```
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{
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"epochs": 20,
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"evaluation_steps": 26,
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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 'torch.optim.adamw.AdamW'>",
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"optimizer_params": {
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"lr": 1e-05
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},
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"scheduler": "WarmupLinear",
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"steps_per_epoch": 253,
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"warmup_steps": 506,
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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': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
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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": "models/bertimbau-335m-europarl-eubookshop-ted2020-tatoeba-ct1-nli-gist10-v3",
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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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"directionality": "bidi",
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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": "4.39.3",
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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": "2.6.1",
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"transformers": "4.39.3",
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"pytorch": "2.2.2+cu121"
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},
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"prompts": {},
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"default_prompt_name": null
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}
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eval/similarity_evaluation_assin-ptbr-test_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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-1,-1,0.8138600919615187,0.7977713687011527,0.8247022497189493,0.8027006473458795,0.8239010357041957,0.801558756548337,0.7812375178467237,0.762358203002527
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eval/similarity_evaluation_assin-ptpt-test_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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-1,-1,0.825843460254658,0.8210186770603135,0.8294873306358647,0.8198745900654728,0.8282092577773816,0.8181867708958612,0.7965664883496952,0.7933136098386931
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eval/similarity_evaluation_assin2-test_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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-1,-1,0.8601521276447252,0.8323475611585354,0.8441468804996282,0.8317085141052287,0.8439260447081153,0.8314017251435526,0.8448990825138911,0.8058027123973086
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eval/similarity_evaluation_iris-sts-test_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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-1,-1,0.8286983072093528,0.8225747363740773,0.8085895174978276,0.8142284490192426,0.8084870772410082,0.8136072215011048,0.822530948426382,0.8219779446427846
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eval/similarity_evaluation_stsb-multi-mt-pt-test_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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-1,-1,0.8444233679612433,0.8485618181849602,0.8319090652861046,0.844440043883229,0.8323194238038486,0.8447479410897777,0.8281878170798076,0.8299955526761081
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eval/similarity_evaluation_validation_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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0,26,0.8404862979733577,0.8442032181881839,0.8233321942780671,0.8393702847595513,0.8228953299754578,0.8389617577273342,0.8330191329592761,0.8340053066924391
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0,52,0.8441385084386361,0.8477703487887954,0.826800475950205,0.8431260815074163,0.8263854850899044,0.8427082177896348,0.8362432724577655,0.837131125238329
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0,78,0.8486071214649209,0.8522597234915239,0.8313892401653161,0.8478122291726976,0.8309784676993136,0.8473402220442637,0.840105702935338,0.8409451945545052
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0,104,0.8521560081816966,0.8561749840177867,0.8356091061122437,0.8520247033195786,0.8352078379711052,0.8515203124519316,0.8424906041597752,0.8432746604350466
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0,130,0.8545523316941874,0.8592546883555083,0.839096722722274,0.8554581673395564,0.8386899758133414,0.8549184099978804,0.8433906100692259,0.844547448896755
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model.safetensors
ADDED
@@ -0,0 +1,3 @@
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|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
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2 |
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oid sha256:de5eef389cc1fce1f8e31e5cdf9ed78432057c84c69a2124ef0c6bd460d2fc99
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3 |
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size 1337630536
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modules.json
ADDED
@@ -0,0 +1,14 @@
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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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"name": "0",
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"path": "",
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"type": "sentence_transformers.models.Transformer"
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},
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{
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"idx": 1,
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"name": "1",
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"path": "1_Pooling",
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12 |
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"type": "sentence_transformers.models.Pooling"
|
13 |
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}
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14 |
+
]
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sentence_bert_config.json
ADDED
@@ -0,0 +1,4 @@
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1 |
+
{
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2 |
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"max_seq_length": 128,
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3 |
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"do_lower_case": false
|
4 |
+
}
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special_tokens_map.json
ADDED
@@ -0,0 +1,37 @@
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{
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},
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},
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"pad_token": {
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"content": "[PAD]",
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|
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|
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|
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|
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},
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"sep_token": {
|
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|
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|
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|
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},
|
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"unk_token": {
|
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"content": "[UNK]",
|
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|
33 |
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|
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"rstrip": false,
|
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"single_word": false
|
36 |
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}
|
37 |
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}
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tokenizer.json
ADDED
The diff for this file is too large to render.
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|
|
tokenizer_config.json
ADDED
@@ -0,0 +1,64 @@
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|
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{
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4 |
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|
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|
18 |
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},
|
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|
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|
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|
26 |
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},
|
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|
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|
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|
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|
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|
33 |
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|
34 |
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},
|
35 |
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|
36 |
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|
37 |
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|
38 |
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|
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|
40 |
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|
41 |
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|
42 |
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}
|
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},
|
44 |
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"clean_up_tokenization_spaces": true,
|
45 |
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|
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"do_basic_tokenize": true,
|
47 |
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|
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|
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|
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"sep_token": "[SEP]",
|
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"stride": 0,
|
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|
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"tokenize_chinese_chars": true,
|
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"tokenizer_class": "BertTokenizer",
|
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"truncation_side": "right",
|
62 |
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"truncation_strategy": "longest_first",
|
63 |
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"unk_token": "[UNK]"
|
64 |
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}
|
train-config.yaml
ADDED
@@ -0,0 +1,12 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
trainer: "sts"
|
2 |
+
model_name: "bertimbau-335m-europarl-eubookshop-ted2020-tatoeba-ct1-nli-gist10-sts-angle20-v3"
|
3 |
+
base_model_name: "bertimbau-335m-europarl-eubookshop-ted2020-tatoeba-ct1-nli-gist10-v3"
|
4 |
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loss_function: "angle"
|
5 |
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seed: 1
|
6 |
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learning_rate: 1e-5
|
7 |
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warmup_ratio: 0.1
|
8 |
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weight_decay: 0.01
|
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batch_size: 75
|
10 |
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use_amp: True
|
11 |
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epochs: 20
|
12 |
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validations_per_epoch: 10
|
vocab.txt
ADDED
The diff for this file is too large to render.
See raw diff
|
|