chk 1800
Browse files- 1_Pooling/config.json +10 -0
- README.md +142 -0
- config_sentence_transformers.json +9 -0
- modules.json +14 -0
- sentence_bert_config.json +4 -0
- trainer_state.json +3279 -0
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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"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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| 1 |
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---
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base_model: BEE-spoke-data/bert-plus-L8-4096-v1.0
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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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license: apache-2.0
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widget:
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- source_sentence: "How to discreetly optimize operating expenses?"
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sentences:
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- "Strategies for quietly reducing overhead costs"
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- "Subtle ways to cut down on operating expenses"
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- "Implementing technology to save on operating costs without broad announcements"
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- "Lowering daily business expenses through unnoticed efficiencies"
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- "Minimizing operational expenditures in small businesses without drawing attention"
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---
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# BEE-spoke-data/bert-plus-L8-v1.0-allNLI_matryoshka
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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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- this was finetuned at 512 ctx (allNLI is all short-ctx examples) but the base model supports 4096
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- Matryoshka dims: [768, 512, 256, 128, 64]
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| 31 |
+
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<!--- Describe your model here -->
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| 33 |
+
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| 34 |
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## Usage (Sentence-Transformers)
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| 36 |
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Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
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| 37 |
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```
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| 39 |
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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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| 43 |
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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('BEE-spoke-data/bert-plus-L8-v1.0-allNLI_matryoshka')
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| 49 |
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embeddings = model.encode(sentences)
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print(embeddings)
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| 51 |
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```
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| 52 |
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## Usage (HuggingFace Transformers)
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| 56 |
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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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| 57 |
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```python
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from transformers import AutoTokenizer, AutoModel
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| 60 |
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import torch
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| 61 |
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| 62 |
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| 63 |
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#Mean Pooling - Take attention mask into account for correct averaging
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| 64 |
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def mean_pooling(model_output, attention_mask):
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| 65 |
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token_embeddings = model_output[0] #First element of model_output contains all token embeddings
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| 66 |
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input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
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| 67 |
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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('BEE-spoke-data/bert-plus-L8-v1.0-allNLI_matryoshka')
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model = AutoModel.from_pretrained('BEE-spoke-data/bert-plus-L8-v1.0-allNLI_matryoshka')
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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=BEE-spoke-data/bert-plus-L8-v1.0-allNLI_matryoshka)
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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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`sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader` of length 8663 with parameters:
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```
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{'batch_size': 32}
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```
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**Loss**:
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`sentence_transformers.losses.MatryoshkaLoss.MatryoshkaLoss` with parameters:
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| 113 |
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```
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{'loss': 'MultipleNegativesRankingLoss', 'matryoshka_dims': [768, 512, 256, 128, 64], 'matryoshka_weights': [1, 1, 1, 1, 1], 'n_dims_per_step': -1}
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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": 1,
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| 121 |
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"evaluation_steps": 216,
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"evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
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| 123 |
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"max_grad_norm": 1,
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| 124 |
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"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
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| 125 |
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"optimizer_params": {
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| 126 |
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"lr": 2e-05
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| 127 |
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},
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| 128 |
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"scheduler": "WarmupLinear",
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| 129 |
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"steps_per_epoch": null,
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| 130 |
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"warmup_steps": 867,
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| 131 |
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"weight_decay": 0.01
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| 132 |
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}
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```
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## Full Model Architecture
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| 137 |
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```
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| 138 |
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SentenceTransformer(
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(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
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| 140 |
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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, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
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| 141 |
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)
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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": "2.5.1",
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"transformers": "4.38.2",
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"pytorch": "2.1.0+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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modules.json
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[
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{
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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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"type": "sentence_transformers.models.Pooling"
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| 13 |
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}
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]
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sentence_bert_config.json
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{
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"max_seq_length": 4096,
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"do_lower_case": false
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}
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trainer_state.json
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