Add new SentenceTransformer model.
Browse files- .gitattributes +1 -0
- 1_Pooling/config.json +9 -0
- README.md +128 -0
- added_tokens.json +7 -0
- config.json +29 -0
- config_sentence_transformers.json +7 -0
- modules.json +14 -0
- pytorch_model.bin +3 -0
- sentence_bert_config.json +4 -0
- sentencepiece.bpe.model +3 -0
- special_tokens_map.json +16 -0
- tokenizer.json +3 -0
- tokenizer_config.json +68 -0
.gitattributes
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@@ -33,3 +33,4 @@ 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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*.zst 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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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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tokenizer.json 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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"pooling_mode_weightedmean_tokens": false,
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"pooling_mode_lasttoken": false
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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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# Wissam42/paraphrase-multilingual-mpnet-base-v2-retuned-fr
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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('Wissam42/paraphrase-multilingual-mpnet-base-v2-retuned-fr')
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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('Wissam42/paraphrase-multilingual-mpnet-base-v2-retuned-fr')
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model = AutoModel.from_pretrained('Wissam42/paraphrase-multilingual-mpnet-base-v2-retuned-fr')
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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=Wissam42/paraphrase-multilingual-mpnet-base-v2-retuned-fr)
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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 360 with parameters:
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```
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{'batch_size': 16, '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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`__main__.CosineSimilarityLoss`
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Parameters of the fit()-Method:
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```
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{
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"epochs": 10,
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"evaluation_steps": 500,
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"evaluator": "__main__.CustomEmbeddingSimilarityEvaluator",
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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-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": 360,
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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: XLMRobertaModel
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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})
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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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added_tokens.json
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{
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"</s>": 2,
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"<mask>": 250001,
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"<pad>": 1,
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"<s>": 0,
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"<unk>": 3
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}
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config.json
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{
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"_name_or_path": "/dbfs/FileStore/shared_uploads/wissam.siblini@komodohealth.com/training_stsbenchmark_sentence-transformers-paraphrase-multilingual-mpnet-base-v2-2024-02-07_19-11-59",
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"architectures": [
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"XLMRobertaModel"
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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": "xlm-roberta",
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"num_attention_heads": 12,
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"num_hidden_layers": 12,
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"output_past": true,
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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": "4.34.0",
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"type_vocab_size": 1,
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"use_cache": true,
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"vocab_size": 250002
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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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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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pytorch_model.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:8016a6d7b50a991f0a4c55d2fc69660f135c178d4b0d9a5c3e579b48c82e1ff6
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size 1112241321
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sentence_bert_config.json
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{
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"max_seq_length": 128,
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"do_lower_case": false
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}
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sentencepiece.bpe.model
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version https://git-lfs.github.com/spec/v1
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oid sha256:cfc8146abe2a0488e9e2a0c56de7952f7c11ab059eca145a0a727afce0db2865
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size 5069051
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special_tokens_map.json
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{
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"additional_special_tokens": [
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"<s>",
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"<pad>",
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"</s>",
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"<unk>",
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"<mask>"
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],
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"bos_token": "<s>",
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"cls_token": "<s>",
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"mask_token": "<mask>",
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"sep_token": "</s>",
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"unk_token": "<unk>"
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}
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tokenizer.json
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version https://git-lfs.github.com/spec/v1
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oid sha256:c835b069d7b8cd02b400e6247b83bc1840ab12bb1628d5b2e03c8d728de75558
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size 17082941
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tokenizer_config.json
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{
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},
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"3": {
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"content": "<unk>",
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"special": true
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}
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},
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"additional_special_tokens": [
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"<s>",
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"<pad>",
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"</s>",
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"<unk>",
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"<mask>"
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],
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"bos_token": "<s>",
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"cls_token": "<s>",
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"pad_token_type_id": 0,
|
61 |
+
"padding_side": "right",
|
62 |
+
"sep_token": "</s>",
|
63 |
+
"stride": 0,
|
64 |
+
"tokenizer_class": "XLMRobertaTokenizer",
|
65 |
+
"truncation_side": "right",
|
66 |
+
"truncation_strategy": "longest_first",
|
67 |
+
"unk_token": "<unk>"
|
68 |
+
}
|