Add new SentenceTransformer model.
Browse files- .gitattributes +1 -0
- 1_Pooling/config.json +7 -0
- README.md +87 -0
- config.json +28 -0
- config_sentence_transformers.json +7 -0
- eval/similarity_evaluation_results.csv +61 -0
- merges.txt +0 -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.json +0 -0
.gitattributes
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@@ -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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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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---
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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 768 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": 5,
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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": 6,
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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': 512, 'do_lower_case': False}) with Transformer model: RobertaModel
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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): Normalize()
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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_all-distilroberta-v1/",
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"architectures": [
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"RobertaModel"
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],
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"attention_probs_dropout_prob": 0.1,
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"bos_token_id": 0,
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"classifier_dropout": null,
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"eos_token_id": 2,
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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-05,
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"max_position_embeddings": 514,
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"model_type": "roberta",
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"num_attention_heads": 12,
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"num_hidden_layers": 6,
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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": 1,
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"use_cache": true,
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"vocab_size": 50265
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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": "4.6.1",
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"pytorch": "1.8.1"
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}
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}
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eval/similarity_evaluation_results.csv
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3,6,0.390142970433912,0.4513866088628077,0.3909848665414643,0.4513866088628077,0.4023433382760454,0.3763674259813833,0.3901433220202359,0.4513866088628077
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44 |
+
3,7,0.3898233197275008,0.4513866088628077,0.39184968331526543,0.4513866088628077,0.40339658365769154,0.43358544682314765,0.38982331923845925,0.4513866088628077
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45 |
+
3,8,0.39114078663148943,0.45774416673411483,0.39422155559123156,0.45774416673411483,0.40585014778149436,0.4361284699716705,0.3911407525228771,0.45774416673411483
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46 |
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3,9,0.39247201421903166,0.45774416673411483,0.39633741086497193,0.45774416673411483,0.4083511282784633,0.4361284699716705,0.39247216614836217,0.45774416673411483
|
47 |
+
3,10,0.39318391318179696,0.45774416673411483,0.39750092632114487,0.45774416673411483,0.4098799510860459,0.4361284699716705,0.39318377270397425,0.45774416673411483
|
48 |
+
3,11,0.38981464605772553,0.4691877709024677,0.3934698895607529,0.4691877709024677,0.4069593003459497,0.4259563773775791,0.38981464263523063,0.4691877709024677
|
49 |
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3,-1,0.38981464605772553,0.4691877709024677,0.3934698895607529,0.4691877709024677,0.4069593003459497,0.4259563773775791,0.38981464263523063,0.4691877709024677
|
50 |
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4,1,0.3872810599641095,0.4691877709024677,0.3903310472411846,0.4691877709024677,0.40474448698348225,0.39798312274382763,0.38728151394494637,0.4691877709024677
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51 |
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4,2,0.3860654851163914,0.4450290509915006,0.3884921345364612,0.4450290509915006,0.4041655351660248,0.39798312274382763,0.38606579738707164,0.4450290509915006
|
52 |
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4,3,0.38428976551180855,0.4450290509915006,0.3861272819475003,0.4450290509915006,0.4022884420953247,0.40179765746661195,0.38428967557696314,0.4450290509915006
|
53 |
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4,4,0.3812122299028358,0.4450290509915006,0.3823736697438846,0.4450290509915006,0.3987868075500073,0.40179765746661195,0.38121225179656865,0.4450290509915006
|
54 |
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4,5,0.37875469706796294,0.4450290509915006,0.3796391328529313,0.4450290509915006,0.39591902635212484,0.40179765746661195,0.37875453589052166,0.4450290509915006
|
55 |
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4,6,0.37645491328584235,0.4450290509915006,0.377424594209919,0.4450290509915006,0.3937943913342341,0.40179765746661195,0.3764547912681717,0.4450290509915006
|
56 |
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4,7,0.37462280362374656,0.4450290509915006,0.3754291660603055,0.4450290509915006,0.39196981061937824,0.40179765746661195,0.37462254235427905,0.4450290509915006
|
57 |
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4,8,0.37326743157189074,0.4450290509915006,0.37409883951281364,0.4450290509915006,0.39077440755871545,0.40179765746661195,0.37326735024676394,0.4450290509915006
|
58 |
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4,9,0.37249660023000075,0.4450290509915006,0.37347105947734077,0.4450290509915006,0.3900649213758474,0.40179765746661195,0.37249633277017347,0.4450290509915006
|
59 |
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4,10,0.37222575714604805,0.4450290509915006,0.37334811969117404,0.4450290509915006,0.38982815409809474,0.39798312274382763,0.37222573984048873,0.4450290509915006
|
60 |
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4,11,0.3726087306044415,0.4450290509915006,0.37385814314501054,0.4450290509915006,0.3902934979208738,0.39798312274382763,0.3726084165143944,0.4450290509915006
|
61 |
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4,-1,0.3726087306044415,0.4450290509915006,0.37385814314501054,0.4450290509915006,0.3902934979208738,0.39798312274382763,0.3726084165143944,0.4450290509915006
|
merges.txt
ADDED
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modules.json
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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 |
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"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 |
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},
|
8 |
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{
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9 |
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"idx": 1,
|
10 |
+
"name": "1",
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11 |
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"path": "1_Pooling",
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12 |
+
"type": "sentence_transformers.models.Pooling"
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13 |
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},
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14 |
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{
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15 |
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"idx": 2,
|
16 |
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"name": "2",
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17 |
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"path": "2_Normalize",
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18 |
+
"type": "sentence_transformers.models.Normalize"
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19 |
+
}
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20 |
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]
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pytorch_model.bin
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1 |
+
version https://git-lfs.github.com/spec/v1
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2 |
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oid sha256:9a6a573653564e2689e7cc9120b0b5ce8521660e3e06e45ea72a67c8186b2e15
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3 |
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size 328517361
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sentence_bert_config.json
ADDED
@@ -0,0 +1,4 @@
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|
|
|
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|
1 |
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{
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2 |
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"max_seq_length": 512,
|
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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|
|
|
|
1 |
+
epoch,steps,cosine_pearson,cosine_spearman,euclidean_pearson,euclidean_spearman,manhattan_pearson,manhattan_spearman,dot_pearson,dot_spearman
|
2 |
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-1,-1,0.7534333699670948,0.34862028508267734,0.7344290336184773,0.34862028508267734,0.732401418683328,0.36200575638800453,0.7534333195675238,0.34855300443390613
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special_tokens_map.json
ADDED
@@ -0,0 +1 @@
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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}}
|
tokenizer.json
ADDED
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|
tokenizer_config.json
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
{"unk_token": "<unk>", "bos_token": "<s>", "eos_token": "</s>", "add_prefix_space": false, "errors": "replace", "sep_token": "</s>", "cls_token": "<s>", "pad_token": "<pad>", "mask_token": "<mask>", "trim_offsets": true, "model_max_length": 512, "special_tokens_map_file": null, "name_or_path": "/root/.cache/torch/sentence_transformers/sentence-transformers_all-distilroberta-v1/", "tokenizer_class": "RobertaTokenizer"}
|
vocab.json
ADDED
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|
|