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 +24 -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": 512, "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 sha256:6d21d46408f5e2936700e803cedb8c73a2b34b9589e311bf98de8dd1cb3d771f
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size 1575975
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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 512 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': 128, 'do_lower_case': False}) with Transformer model: DistilBertModel
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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': 512, '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": "/root/.cache/torch/sentence_transformers/sentence-transformers_distiluse-base-multilingual-cased-v1/",
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"activation": "gelu",
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"architectures": [
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"DistilBertModel"
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],
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"attention_dropout": 0.1,
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"dim": 768,
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"dropout": 0.1,
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"hidden_dim": 3072,
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"initializer_range": 0.02,
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"max_position_embeddings": 512,
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"model_type": "distilbert",
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"n_heads": 12,
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"n_layers": 6,
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"pad_token_id": 0,
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"qa_dropout": 0.1,
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"seq_classif_dropout": 0.2,
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"sinusoidal_pos_embds": false,
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"tie_weights_": true,
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"torch_dtype": "float32",
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"transformers_version": "4.16.2",
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"vocab_size": 119547
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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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|
1 |
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epoch,steps,cosine_pearson,cosine_spearman,euclidean_pearson,euclidean_spearman,manhattan_pearson,manhattan_spearman,dot_pearson,dot_spearman
|
2 |
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0,1,0.3025718391868595,0.4246848658033177,0.30613216767618595,0.3839964954269519,0.291456965945577,0.33695056717927896,0.31795452080383496,0.37382440283286045
|
3 |
+
0,2,0.3017841003979427,0.4246848658033177,0.305522432005631,0.3839964954269519,0.2908577708062319,0.33695056717927896,0.3172848924293299,0.37382440283286045
|
4 |
+
0,3,0.30037819008697725,0.381453472278429,0.30440546381016753,0.3839964954269519,0.2899023769828476,0.33695056717927896,0.3161170405534912,0.39544009959530474
|
5 |
+
0,4,0.2981796248788889,0.4068837037636577,0.302734478978213,0.3839964954269519,0.2885690730347374,0.33695056717927896,0.3138003733382661,0.39544009959530474
|
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506 |
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508 |
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509 |
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510 |
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512 |
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514 |
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518 |
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modules.json
ADDED
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[
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|
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+
"type": "sentence_transformers.models.Dense"
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+
}
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+
]
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pytorch_model.bin
ADDED
@@ -0,0 +1,3 @@
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|
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|
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|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:a9382626b72a241bfc2fbf25fda0dbd5d9e1f96164ef7eedb1000586fe409997
|
3 |
+
size 538972985
|
sentence_bert_config.json
ADDED
@@ -0,0 +1,4 @@
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|
|
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|
1 |
+
{
|
2 |
+
"max_seq_length": 128,
|
3 |
+
"do_lower_case": false
|
4 |
+
}
|
similarity_evaluation_sts-test_results.csv
ADDED
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|
1 |
+
epoch,steps,cosine_pearson,cosine_spearman,euclidean_pearson,euclidean_spearman,manhattan_pearson,manhattan_spearman,dot_pearson,dot_spearman
|
2 |
+
-1,-1,0.7807156758742617,0.48130497383017473,0.7675078104370313,0.48356378237100783,0.761237119391385,0.4841650479538195,0.7786589649087597,0.45972441453358237
|
special_tokens_map.json
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
{"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"}
|
tokenizer.json
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tokenizer_config.json
ADDED
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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, "max_len": 512, "special_tokens_map_file": "old_models/distiluse-base-multilingual-cased-v1/0_Transformer/special_tokens_map.json", "name_or_path": "/root/.cache/torch/sentence_transformers/sentence-transformers_distiluse-base-multilingual-cased-v1/", "do_basic_tokenize": true, "never_split": null, "tokenizer_class": "DistilBertTokenizer"}
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vocab.txt
ADDED
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