Add new SentenceTransformer model.
Browse files- .gitattributes +1 -0
- 1_Pooling/config.json +7 -0
- 2_Dense/config.json +1 -0
- 2_Dense/pytorch_model.bin +3 -0
- README.md +87 -0
- config.json +27 -0
- config_sentence_transformers.json +7 -0
- eval/similarity_evaluation_results.csv +601 -0
- modules.json +20 -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 +0 -0
- tokenizer_config.json +1 -0
- vocab.txt +0 -0
.gitattributes
CHANGED
@@ -25,3 +25,4 @@ 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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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": 768,
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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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2_Dense/config.json
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{"in_features": 768, "out_features": 256, "bias": true, "activation_function": "torch.nn.modules.activation.Tanh"}
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2_Dense/pytorch_model.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:d6edced968386e635167f1c9317f42ddda767ca74b74672656a28888a167129f
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size 788519
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README.md
ADDED
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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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---
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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 256 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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## 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": 50,
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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": 55,
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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': 256, 'do_lower_case': False}) with Transformer model: BertModel
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(1): Pooling({'word_embedding_dimension': 768, '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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(2): Dense({'in_features': 768, 'out_features': 256, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'})
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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": "dccuchile/bert-base-spanish-wwm-uncased",
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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": 768,
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"initializer_range": 0.02,
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"intermediate_size": 3072,
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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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"output_past": true,
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"pad_token_id": 1,
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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": 31002
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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": "2.2.0",
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"transformers": "4.16.2",
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"pytorch": "1.10.0+cu111"
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}
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}
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eval/similarity_evaluation_results.csv
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1 |
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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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0,1,0.19815413819334904,0.17165406252529306,0.17760777215434015,0.0673901134358558,0.19799428877961667,0.13986627316875733,0.04371014336764901,0.3382220787535404
|
3 |
+
0,2,0.2000883223342818,0.17165406252529306,0.1788828706238412,0.0673901134358558,0.19933041829110598,0.13986627316875733,0.04552341204451381,0.3382220787535404
|
4 |
+
0,3,0.20338320242609711,0.1894552245649531,0.18125970775772393,0.0673901134358558,0.2018309585795476,0.13986627316875733,0.04806920461375693,0.3382220787535404
|
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371 |
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373 |
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378 |
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379 |
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381 |
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382 |
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383 |
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385 |
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397 |
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398 |
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403 |
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404 |
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405 |
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408 |
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409 |
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410 |
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411 |
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412 |
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413 |
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414 |
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415 |
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416 |
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417 |
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418 |
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419 |
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420 |
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421 |
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422 |
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423 |
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424 |
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425 |
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426 |
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427 |
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428 |
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429 |
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430 |
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431 |
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432 |
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433 |
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434 |
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435 |
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436 |
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437 |
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438 |
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479 |
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480 |
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481 |
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484 |
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493 |
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494 |
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496 |
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497 |
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498 |
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500 |
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501 |
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502 |
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503 |
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504 |
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505 |
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506 |
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507 |
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508 |
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509 |
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510 |
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511 |
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512 |
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513 |
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514 |
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515 |
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516 |
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517 |
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518 |
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519 |
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520 |
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521 |
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522 |
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523 |
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524 |
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modules.json
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[
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"name": "1",
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pytorch_model.bin
ADDED
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version https://git-lfs.github.com/spec/v1
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size 439484849
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sentence_bert_config.json
ADDED
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{
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similarity_evaluation_sts-test_results.csv
ADDED
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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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2 |
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-1,-1,0.812068911630854,0.5607370324459454,0.8236120477085126,0.6014768382867263,0.8195565473023911,0.5858926843970936,0.7476579714001064,0.4124519393876584
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special_tokens_map.json
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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
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tokenizer_config.json
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{"do_lower_case": true, "unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]", "tokenize_chinese_chars": true, "strip_accents": false, "do_basic_tokenize": true, "never_split": null, "model_max_length": 512, "special_tokens_map_file": "/root/.cache/huggingface/transformers/78141ed1e8dcc5ff370950397ca0d1c5c9da478f54ec14544187d8a93eff1a26.f982506b52498d4adb4bd491f593dc92b2ef6be61bfdbe9d30f53f963f9f5b66", "name_or_path": "dccuchile/bert-base-spanish-wwm-uncased", "tokenizer_class": "BertTokenizer"}
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vocab.txt
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