Upload with huggingface_hub
Browse files- 0_SentenceTransformer/1_Pooling/config.json +7 -0
- 0_SentenceTransformer/README.md +80 -0
- 0_SentenceTransformer/config.json +25 -0
- 0_SentenceTransformer/config_sentence_transformers.json +7 -0
- 0_SentenceTransformer/modules.json +14 -0
- 0_SentenceTransformer/pytorch_model.bin +3 -0
- 0_SentenceTransformer/sentence_bert_config.json +4 -0
- 0_SentenceTransformer/special_tokens_map.json +7 -0
- 0_SentenceTransformer/tokenizer.json +0 -0
- 0_SentenceTransformer/tokenizer_config.json +17 -0
- 0_SentenceTransformer/vocab.txt +0 -0
- 1_Dense/config.json +1 -0
- 1_Dense/pytorch_model.bin +3 -0
- README.md +91 -0
- config_sentence_transformers.json +7 -0
- eval/mse_evaluation__results.csv +29 -0
- eval/similarity_evaluation_sts-dev_results.csv +29 -0
- modules.json +14 -0
0_SentenceTransformer/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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0_SentenceTransformer/README.md
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---
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pipeline_tag: sentence-similarity
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license: apache-2.0
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language:
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- it
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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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# sentence-BERTino
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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. It was trained on a dataset made from question/context pairs ([squad-it](https://github.com/crux82/squad-it)) and tags/news-article pairs (via scraping).
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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 = ["Questo è un esempio di frase", "Questo è un ulteriore esempio"]
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model = SentenceTransformer('efederici/sentence-BERTino')
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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 = ["Questo è un esempio di frase", "Questo è un ulteriore esempio"]
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# Load model from HuggingFace Hub
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tokenizer = AutoTokenizer.from_pretrained('efederici/sentence-BERTino')
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model = AutoModel.from_pretrained('efederici/sentence-BERTino')
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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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## 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: 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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)
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```
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0_SentenceTransformer/config.json
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{
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"_name_or_path": "/root/.cache/torch/sentence_transformers/efederici_sentence-BERTino/",
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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": 3,
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"output_hidden_states": true,
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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": "4.23.1",
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"vocab_size": 32102
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}
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0_SentenceTransformer/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": "4.17.0",
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"pytorch": "1.10.0+cu111"
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}
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}
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0_SentenceTransformer/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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}
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]
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0_SentenceTransformer/pytorch_model.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:edeaf53e3122eab7cf32f5f3deaaf2710c440a7b0da5afe4c281673ee0830667
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size 185267017
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0_SentenceTransformer/sentence_bert_config.json
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{
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"max_seq_length": 512,
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"do_lower_case": false
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}
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0_SentenceTransformer/special_tokens_map.json
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{
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"cls_token": "[CLS]",
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"mask_token": "[MASK]",
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"pad_token": "[PAD]",
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"sep_token": "[SEP]",
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"unk_token": "[UNK]"
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}
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0_SentenceTransformer/tokenizer.json
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See raw diff
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0_SentenceTransformer/tokenizer_config.json
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{
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"cls_token": "[CLS]",
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"do_basic_tokenize": true,
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"do_lower_case": true,
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"full_tokenizer_file": null,
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"mask_token": "[MASK]",
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"max_len": 512,
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"name_or_path": "/root/.cache/torch/sentence_transformers/efederici_sentence-BERTino/",
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"never_split": null,
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"pad_token": "[PAD]",
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"sep_token": "[SEP]",
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"special_tokens_map_file": null,
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"strip_accents": null,
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"tokenize_chinese_chars": true,
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"tokenizer_class": "DistilBertTokenizer",
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"unk_token": "[UNK]"
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}
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0_SentenceTransformer/vocab.txt
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1_Dense/config.json
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{"in_features": 768, "out_features": 64, "bias": true, "activation_function": "torch.nn.modules.activation.Tanh"}
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1_Dense/pytorch_model.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:239c2707843befbc4780fe214749fc03e0614fdc7e9255dadafe47beae81a24f
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size 197927
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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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6 |
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- sentence-similarity
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|
8 |
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---
|
9 |
+
|
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# {MODEL_NAME}
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+
|
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This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 64 dimensional dense vector space and can be used for tasks like clustering or semantic search.
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13 |
+
|
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+
<!--- Describe your model here -->
|
15 |
+
|
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+
## Usage (Sentence-Transformers)
|
17 |
+
|
18 |
+
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
|
19 |
+
|
20 |
+
```
|
21 |
+
pip install -U sentence-transformers
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```
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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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|
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`torch.utils.data.dataloader.DataLoader` of length 1724 with parameters:
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```
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{'batch_size': 64, '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.MSELoss.MSELoss`
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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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"evaluation_steps": 500,
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"evaluator": "sentence_transformers.evaluation.SequentialEvaluator.SequentialEvaluator",
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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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"eps": 1e-06,
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"lr": 1e-07
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},
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"scheduler": "WarmupLinear",
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"steps_per_epoch": null,
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"warmup_steps": 100,
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"weight_decay": 0.01
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}
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```
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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): SentenceTransformer(
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(0): Transformer({'max_seq_length': 512, '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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)
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(1): Dense({'in_features': 768, 'out_features': 64, '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_sentence_transformers.json
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{
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"__version__": {
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"sentence_transformers": "2.2.2",
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"transformers": "4.23.1",
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"pytorch": "1.12.1+cu113"
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}
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}
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eval/mse_evaluation__results.csv
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epoch,steps,MSE
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0,500,160.0018858909607
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0,1000,157.27769136428833
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4 |
+
0,1500,155.6653618812561
|
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0,-1,155.4488182067871
|
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0,500,98.15781712532043
|
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0,1000,76.94787383079529
|
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0,1500,68.42235922813416
|
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|
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1,500,63.229817152023315
|
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1,1000,61.31596565246582
|
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1,1500,60.14971137046814
|
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1,-1,59.68950390815735
|
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2,500,59.01828408241272
|
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|
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|
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|
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0,500,58.216989040374756
|
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0,1000,58.06503891944885
|
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0,1500,57.93534517288208
|
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0,-1,57.89196491241455
|
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1,500,57.828253507614136
|
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1,1000,57.760089635849
|
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1,1500,57.728540897369385
|
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|
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|
28 |
+
0,1500,57.697200775146484
|
29 |
+
0,-1,57.69643187522888
|
eval/similarity_evaluation_sts-dev_results.csv
ADDED
@@ -0,0 +1,29 @@
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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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0,500,0.5865738040483492,0.6080073489584343,0.6135697139081404,0.6209144397277622,0.6080297443609035,0.6150423947449148,0.30760717559007184,0.31234556074873465
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0,1000,0.5932329861734787,0.6145486559695778,0.6209473347843074,0.6285679752654623,0.6156857991443561,0.6230859536837934,0.2902995170696891,0.29410016166955844
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0,1500,0.5997207615500473,0.6197545563640058,0.6266288668984643,0.6333906867830073,0.6216103921025053,0.6283979609131071,0.29927639170944115,0.3039065427395013
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0,-1,0.6004929935405958,0.6203905760382272,0.6272614520062297,0.634024206793723,0.622256060456697,0.6291191927282038,0.3009699947338997,0.3056782821262222
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0,500,0.6082876631364406,0.6383080638463929,0.6713194802395258,0.6666769766877335,0.6696520375976636,0.6676769987310615,0.4984370002871881,0.48647455814121354
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0,1500,0.6938622625314218,0.7027819962982079,0.7300484668389849,0.7227559033386031,0.7239534109735188,0.7171358229426745,0.6432735292808788,0.6345696105211257
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1,500,0.7026776271111842,0.7103170015048932,0.7351407131757489,0.7278891702308841,0.7283502666392208,0.7219225344353434,0.6598208883116933,0.6523325409289799
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1,1000,0.7051954773480473,0.712368926410317,0.7357815021563106,0.7286132668166436,0.7292599158711848,0.7226115402495245,0.6661954715118892,0.6596294534379665
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1,1500,0.7082188011794502,0.7152173340725155,0.7378006755328732,0.7308711900850527,0.7315619929431638,0.7252391043427502,0.6710356608646818,0.6647982399231508
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|
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2,500,0.7100771821625964,0.7167180521913615,0.7391966431541905,0.7324477676869227,0.7327015552647981,0.7262537331577372,0.6736893242334752,0.667143259335068
|
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2,1000,0.7108737865640337,0.7169675381288808,0.7395318432365394,0.7325689012225832,0.7331158055268099,0.726662598208946,0.6749191654461464,0.6684612228780606
|
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2,1500,0.7117215803275778,0.7177861472354646,0.7399094584535856,0.7329357199813928,0.7336377259374134,0.7272406822382951,0.6764804931321545,0.6700393218318923
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0,500,0.7121460455239379,0.7181123574252887,0.7399511395506606,0.7331083588907218,0.7336774177428855,0.7272558299355949,0.6771142222790999,0.6705752866145787
|
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0,1000,0.7123861478820172,0.7181627442194691,0.7400716088926611,0.7330949496287871,0.7338919056678781,0.727296143130711,0.6775155455308138,0.6708743366430204
|
20 |
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0,1500,0.7122284907285029,0.7180972453152019,0.7401198061461532,0.7332444924367473,0.7337385450265813,0.7272772676728283,0.6771726702603322,0.6703615444741903
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|
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1,500,0.712251279207934,0.7180605495603078,0.7399009046552868,0.7328821274176701,0.7337147222065724,0.7271828049741026,0.6775682024150373,0.6710392299952491
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1,1000,0.7125332144097017,0.7182585034337489,0.7401266535032988,0.7332453749996348,0.7339200062049424,0.7273773976352566,0.6778856175804594,0.6712869419099701
|
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1,1500,0.7126742859770369,0.7183989294362636,0.740284929037369,0.7333443804061257,0.7340499087406678,0.7274551307850554,0.6779089373279104,0.6712837070322928
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1,-1,0.712748264487567,0.7184477986871575,0.7403131695377074,0.7333492451781706,0.7340709425847874,0.7274524030251635,0.6779903965891169,0.6714038352290735
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0,500,0.7126967895258003,0.7184174555008799,0.7402840486509272,0.733308238032397,0.7340461198296704,0.7274432339796818,0.6779725544896934,0.6713037515301116
|
27 |
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0,1000,0.712916278926716,0.7186633827170787,0.7404429389807402,0.7335128164655754,0.7342023524428132,0.727619871112414,0.678277663787251,0.6716970293815879
|
28 |
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0,1500,0.7128783807214034,0.7186522314638217,0.7404290748112105,0.7334913484792108,0.7341734466453587,0.727559420892707,0.6782398243891938,0.6716509314850065
|
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0,-1,0.712884474626804,0.7186568382285708,0.7404293672433467,0.7334823929570085,0.7341750526470242,0.7275848746469504,0.678234793119852,0.6716365702651312
|
modules.json
ADDED
@@ -0,0 +1,14 @@
|
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|
|
|
|
|
1 |
+
[
|
2 |
+
{
|
3 |
+
"idx": 0,
|
4 |
+
"name": "0",
|
5 |
+
"path": "0_SentenceTransformer",
|
6 |
+
"type": "sentence_transformers.SentenceTransformer"
|
7 |
+
},
|
8 |
+
{
|
9 |
+
"idx": 1,
|
10 |
+
"name": "1",
|
11 |
+
"path": "1_Dense",
|
12 |
+
"type": "sentence_transformers.models.Dense"
|
13 |
+
}
|
14 |
+
]
|