Upload folder using huggingface_hub (#1)
Browse files- Upload folder using huggingface_hub (b88e970c85d6847008f773138b04f6561eac0ef9)
- 1_Pooling/config.json +9 -8
- README.md +131 -131
- config.json +24 -24
- config_sentence_transformers.json +13 -6
- modules.json +13 -13
- onnx/model.onnx +3 -0
- onnx/model_O1.onnx +3 -0
- onnx/model_O2.onnx +3 -0
- onnx/model_O3.onnx +3 -0
- onnx/model_O4.onnx +3 -0
- onnx/model_qint8_arm64.onnx +3 -0
- onnx/model_qint8_avx512.onnx +3 -0
- onnx/model_qint8_avx512_vnni.onnx +3 -0
- onnx/model_quint8_avx2.onnx +3 -0
- openvino/openvino_model.bin +3 -0
- openvino/openvino_model.xml +0 -0
- sentence_bert_config.json +3 -3
- special_tokens_map.json +51 -51
- tokenizer_config.json +73 -72
1_Pooling/config.json
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{
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}
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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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---
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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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---
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# tomaarsen/mpnet-base-nli-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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<!--- 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('tomaarsen/mpnet-base-nli-matryoshka')
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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 = ['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('tomaarsen/mpnet-base-nli-matryoshka')
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model = AutoModel.from_pretrained('tomaarsen/mpnet-base-nli-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=tomaarsen/mpnet-base-nli-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 8807 with parameters:
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```
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{'batch_size': 64}
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```
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**Loss**:
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`sentence_transformers.losses.MatryoshkaLoss.MatryoshkaLoss` with parameters:
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```
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{'loss': 'MultipleNegativesRankingLoss', 'matryoshka_dims': [768, 512, 256, 128, 64], 'matryoshka_weights': [1, 1, 1, 1, 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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"evaluation_steps": 880,
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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 'torch.optim.adamw.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": 881,
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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': 75, 'do_lower_case': False}) with Transformer model: MPNetModel
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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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)
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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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---
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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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| 8 |
+
- transformers
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| 9 |
+
|
| 10 |
+
---
|
| 11 |
+
|
| 12 |
+
# tomaarsen/mpnet-base-nli-matryoshka
|
| 13 |
+
|
| 14 |
+
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.
|
| 15 |
+
|
| 16 |
+
|
| 17 |
+
|
| 18 |
+
<!--- Describe your model here -->
|
| 19 |
+
|
| 20 |
+
## Usage (Sentence-Transformers)
|
| 21 |
+
|
| 22 |
+
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
|
| 23 |
+
|
| 24 |
+
```
|
| 25 |
+
pip install -U sentence-transformers
|
| 26 |
+
```
|
| 27 |
+
|
| 28 |
+
Then you can use the model like this:
|
| 29 |
+
|
| 30 |
+
```python
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| 31 |
+
from sentence_transformers import SentenceTransformer
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+
sentences = ["This is an example sentence", "Each sentence is converted"]
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+
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model = SentenceTransformer('tomaarsen/mpnet-base-nli-matryoshka')
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embeddings = model.encode(sentences)
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print(embeddings)
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```
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+
|
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+
|
| 40 |
+
|
| 41 |
+
## Usage (HuggingFace Transformers)
|
| 42 |
+
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.
|
| 43 |
+
|
| 44 |
+
```python
|
| 45 |
+
from transformers import AutoTokenizer, AutoModel
|
| 46 |
+
import torch
|
| 47 |
+
|
| 48 |
+
|
| 49 |
+
#Mean Pooling - Take attention mask into account for correct averaging
|
| 50 |
+
def mean_pooling(model_output, attention_mask):
|
| 51 |
+
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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+
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+
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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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| 58 |
+
|
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+
# Load model from HuggingFace Hub
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+
tokenizer = AutoTokenizer.from_pretrained('tomaarsen/mpnet-base-nli-matryoshka')
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model = AutoModel.from_pretrained('tomaarsen/mpnet-base-nli-matryoshka')
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+
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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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+
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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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+
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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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+
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print("Sentence embeddings:")
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print(sentence_embeddings)
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```
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+
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+
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+
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## Evaluation Results
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| 80 |
+
|
| 81 |
+
<!--- Describe how your model was evaluated -->
|
| 82 |
+
|
| 83 |
+
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=tomaarsen/mpnet-base-nli-matryoshka)
|
| 84 |
+
|
| 85 |
+
|
| 86 |
+
## Training
|
| 87 |
+
The model was trained with the parameters:
|
| 88 |
+
|
| 89 |
+
**DataLoader**:
|
| 90 |
+
|
| 91 |
+
`sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader` of length 8807 with parameters:
|
| 92 |
+
```
|
| 93 |
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{'batch_size': 64}
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```
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+
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**Loss**:
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| 97 |
+
|
| 98 |
+
`sentence_transformers.losses.MatryoshkaLoss.MatryoshkaLoss` with parameters:
|
| 99 |
+
```
|
| 100 |
+
{'loss': 'MultipleNegativesRankingLoss', 'matryoshka_dims': [768, 512, 256, 128, 64], 'matryoshka_weights': [1, 1, 1, 1, 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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"evaluation_steps": 880,
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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 'torch.optim.adamw.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": 881,
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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): Transformer({'max_seq_length': 75, 'do_lower_case': False}) with Transformer model: MPNetModel
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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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)
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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": "
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"architectures": [
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"MPNetModel"
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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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"eos_token_id": 2,
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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": "mpnet",
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"num_attention_heads": 12,
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"num_hidden_layers": 12,
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"pad_token_id": 1,
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"relative_attention_num_buckets": 32,
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"torch_dtype": "float32",
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"transformers_version": "4.
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"vocab_size": 30527
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}
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{
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"_name_or_path": "tomaarsen/mpnet-base-nli-matryoshka",
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"architectures": [
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"MPNetModel"
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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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"eos_token_id": 2,
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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": "mpnet",
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"num_attention_heads": 12,
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"num_hidden_layers": 12,
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"pad_token_id": 1,
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"relative_attention_num_buckets": 32,
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"torch_dtype": "float32",
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"transformers_version": "4.48.3",
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"vocab_size": 30527
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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.
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"transformers": "4.
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"pytorch": "2.
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}
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"__version__": {
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"sentence_transformers": "4.2.0.dev0",
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"transformers": "4.48.3",
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"pytorch": "2.6.0+cu124"
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},
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"model_type": "SentenceTransformer",
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"prompts": {
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"query": "",
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"document": ""
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},
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"default_prompt_name": null,
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"similarity_fn_name": "cosine"
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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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"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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[
|
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sentence_bert_config.json
CHANGED
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special_tokens_map.json
CHANGED
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CHANGED
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| 1 |
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| 2 |
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