Add new SentenceTransformer model.

0_SentenceTransformer/1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "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
+}

0_SentenceTransformer/README.md

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+---
+language: en
+license: apache-2.0
+library_name: sentence-transformers
+tags:
+- sentence-transformers
+- feature-extraction
+- sentence-similarity
+- transformers
+datasets:
+- s2orc
+- flax-sentence-embeddings/stackexchange_xml
+- ms_marco
+- gooaq
+- yahoo_answers_topics
+- code_search_net
+- search_qa
+- eli5
+- snli
+- multi_nli
+- wikihow
+- natural_questions
+- trivia_qa
+- embedding-data/sentence-compression
+- embedding-data/flickr30k-captions
+- embedding-data/altlex
+- embedding-data/simple-wiki
+- embedding-data/QQP
+- embedding-data/SPECTER
+- embedding-data/PAQ_pairs
+- embedding-data/WikiAnswers
+pipeline_tag: sentence-similarity
+---
+
+
+# all-mpnet-base-v2
+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.
+
+## Usage (Sentence-Transformers)
+Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
+
+```
+pip install -U sentence-transformers
+```
+
+Then you can use the model like this:
+```python
+from sentence_transformers import SentenceTransformer
+sentences = ["This is an example sentence", "Each sentence is converted"]
+
+model = SentenceTransformer('sentence-transformers/all-mpnet-base-v2')
+embeddings = model.encode(sentences)
+print(embeddings)
+```
+
+## Usage (HuggingFace Transformers)
+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.
+
+```python
+from transformers import AutoTokenizer, AutoModel
+import torch
+import torch.nn.functional as F
+
+#Mean Pooling - Take attention mask into account for correct averaging
+def mean_pooling(model_output, attention_mask):
+    token_embeddings = model_output[0] #First element of model_output contains all token embeddings
+    input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
+    return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
+
+
+# Sentences we want sentence embeddings for
+sentences = ['This is an example sentence', 'Each sentence is converted']
+
+# Load model from HuggingFace Hub
+tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/all-mpnet-base-v2')
+model = AutoModel.from_pretrained('sentence-transformers/all-mpnet-base-v2')
+
+# Tokenize sentences
+encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
+
+# Compute token embeddings
+with torch.no_grad():
+    model_output = model(**encoded_input)
+
+# Perform pooling
+sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
+
+# Normalize embeddings
+sentence_embeddings = F.normalize(sentence_embeddings, p=2, dim=1)
+
+print("Sentence embeddings:")
+print(sentence_embeddings)
+```
+
+## Evaluation Results
+
+For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=sentence-transformers/all-mpnet-base-v2)
+
+------
+
+## Background
+
+The project aims to train sentence embedding models on very large sentence level datasets using a self-supervised 
+contrastive learning objective. We used the pretrained [`microsoft/mpnet-base`](https://huggingface.co/microsoft/mpnet-base) model and fine-tuned in on a 
+1B sentence pairs dataset. We use a contrastive learning objective: given a sentence from the pair, the model should predict which out of a set of randomly sampled other sentences, was actually paired with it in our dataset.
+
+We developped this model during the 
+[Community week using JAX/Flax for NLP & CV](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/7104), 
+organized by Hugging Face. We developped this model as part of the project:
+[Train the Best Sentence Embedding Model Ever with 1B Training Pairs](https://discuss.huggingface.co/t/train-the-best-sentence-embedding-model-ever-with-1b-training-pairs/7354). We benefited from efficient hardware infrastructure to run the project: 7 TPUs v3-8, as well as intervention from Googles Flax, JAX, and Cloud team member about efficient deep learning frameworks.
+
+## Intended uses
+
+Our model is intented to be used as a sentence and short paragraph encoder. Given an input text, it ouptuts a vector which captures 
+the semantic information. The sentence vector may be used for information retrieval, clustering or sentence similarity tasks.
+
+By default, input text longer than 384 word pieces is truncated.
+
+
+## Training procedure
+
+### Pre-training 
+
+We use the pretrained [`microsoft/mpnet-base`](https://huggingface.co/microsoft/mpnet-base) model. Please refer to the model card for more detailed information about the pre-training procedure.
+
+### Fine-tuning 
+
+We fine-tune the model using a contrastive objective. Formally, we compute the cosine similarity from each possible sentence pairs from the batch.
+We then apply the cross entropy loss by comparing with true pairs.
+
+#### Hyper parameters
+
+We trained ou model on a TPU v3-8. We train the model during 100k steps using a batch size of 1024 (128 per TPU core).
+We use a learning rate warm up of 500. The sequence length was limited to 128 tokens. We used the AdamW optimizer with
+a 2e-5 learning rate. The full training script is accessible in this current repository: `train_script.py`.
+
+#### Training data
+
+We use the concatenation from multiple datasets to fine-tune our model. The total number of sentence pairs is above 1 billion sentences.
+We sampled each dataset given a weighted probability which configuration is detailed in the `data_config.json` file.
+
+
+| Dataset                                                  | Paper                                    | Number of training tuples  |
+|--------------------------------------------------------|:----------------------------------------:|:--------------------------:|
+| [Reddit comments (2015-2018)](https://github.com/PolyAI-LDN/conversational-datasets/tree/master/reddit) | [paper](https://arxiv.org/abs/1904.06472) | 726,484,430 |
+| [S2ORC](https://github.com/allenai/s2orc) Citation pairs (Abstracts) | [paper](https://aclanthology.org/2020.acl-main.447/) | 116,288,806 |
+| [WikiAnswers](https://github.com/afader/oqa#wikianswers-corpus) Duplicate question pairs | [paper](https://doi.org/10.1145/2623330.2623677) | 77,427,422 |
+| [PAQ](https://github.com/facebookresearch/PAQ) (Question, Answer) pairs | [paper](https://arxiv.org/abs/2102.07033) | 64,371,441 |
+| [S2ORC](https://github.com/allenai/s2orc) Citation pairs (Titles) | [paper](https://aclanthology.org/2020.acl-main.447/) | 52,603,982 |
+| [S2ORC](https://github.com/allenai/s2orc) (Title, Abstract) | [paper](https://aclanthology.org/2020.acl-main.447/) | 41,769,185 |
+| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title, Body) pairs  | - | 25,316,456 |
+| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title+Body, Answer) pairs  | - | 21,396,559 |
+| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title, Answer) pairs  | - | 21,396,559 |
+| [MS MARCO](https://microsoft.github.io/msmarco/) triplets | [paper](https://doi.org/10.1145/3404835.3462804) | 9,144,553 |
+| [GOOAQ: Open Question Answering with Diverse Answer Types](https://github.com/allenai/gooaq) | [paper](https://arxiv.org/pdf/2104.08727.pdf) | 3,012,496 |
+| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Title, Answer) | [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) | 1,198,260 |
+| [Code Search](https://huggingface.co/datasets/code_search_net) | - | 1,151,414 |
+| [COCO](https://cocodataset.org/#home) Image captions | [paper](https://link.springer.com/chapter/10.1007%2F978-3-319-10602-1_48) | 828,395|
+| [SPECTER](https://github.com/allenai/specter) citation triplets | [paper](https://doi.org/10.18653/v1/2020.acl-main.207) | 684,100 |
+| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Question, Answer) | [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) | 681,164 |
+| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Title, Question) | [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) | 659,896 |
+| [SearchQA](https://huggingface.co/datasets/search_qa) | [paper](https://arxiv.org/abs/1704.05179) | 582,261 |
+| [Eli5](https://huggingface.co/datasets/eli5) | [paper](https://doi.org/10.18653/v1/p19-1346) | 325,475 |
+| [Flickr 30k](https://shannon.cs.illinois.edu/DenotationGraph/) | [paper](https://transacl.org/ojs/index.php/tacl/article/view/229/33) | 317,695 |
+| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) Duplicate questions (titles) | | 304,525 |
+| AllNLI ([SNLI](https://nlp.stanford.edu/projects/snli/) and [MultiNLI](https://cims.nyu.edu/~sbowman/multinli/) | [paper SNLI](https://doi.org/10.18653/v1/d15-1075), [paper MultiNLI](https://doi.org/10.18653/v1/n18-1101) | 277,230 | 
+| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) Duplicate questions (bodies) | | 250,519 |
+| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) Duplicate questions (titles+bodies) | | 250,460 |
+| [Sentence Compression](https://github.com/google-research-datasets/sentence-compression) | [paper](https://www.aclweb.org/anthology/D13-1155/) | 180,000 |
+| [Wikihow](https://github.com/pvl/wikihow_pairs_dataset) | [paper](https://arxiv.org/abs/1810.09305) | 128,542 |
+| [Altlex](https://github.com/chridey/altlex/) | [paper](https://aclanthology.org/P16-1135.pdf) | 112,696 |
+| [Quora Question Triplets](https://quoradata.quora.com/First-Quora-Dataset-Release-Question-Pairs) | - | 103,663 |
+| [Simple Wikipedia](https://cs.pomona.edu/~dkauchak/simplification/) | [paper](https://www.aclweb.org/anthology/P11-2117/) | 102,225 |
+| [Natural Questions (NQ)](https://ai.google.com/research/NaturalQuestions) | [paper](https://transacl.org/ojs/index.php/tacl/article/view/1455) | 100,231 |
+| [SQuAD2.0](https://rajpurkar.github.io/SQuAD-explorer/) | [paper](https://aclanthology.org/P18-2124.pdf) | 87,599 |
+| [TriviaQA](https://huggingface.co/datasets/trivia_qa) | - | 73,346 |
+| **Total** | | **1,170,060,424** |

0_SentenceTransformer/config.json

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+{
+  "_name_or_path": "/models/0_SentenceTransformer",
+  "architectures": [
+    "MPNetModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "mpnet",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 1,
+  "relative_attention_num_buckets": 32,
+  "torch_dtype": "float32",
+  "transformers_version": "4.47.1",
+  "vocab_size": 30527
+}

0_SentenceTransformer/config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.3.1",
+    "transformers": "4.47.1",
+    "pytorch": "2.5.1+cu124"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

0_SentenceTransformer/model.safetensors

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+size 437967672

0_SentenceTransformer/modules.json

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+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  },
+  {
+    "idx": 2,
+    "name": "2",
+    "path": "2_Normalize",
+    "type": "sentence_transformers.models.Normalize"
+  }
+]

0_SentenceTransformer/sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+  "max_seq_length": 384,
+  "do_lower_case": false
+}

0_SentenceTransformer/special_tokens_map.json

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+{
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "cls_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "<mask>",
+    "lstrip": true,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<pad>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

0_SentenceTransformer/tokenizer_config.json

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+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
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+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "</s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "3": {
+      "content": "<unk>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "104": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "30526": {
+      "content": "<mask>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": false,
+  "cls_token": "<s>",
+  "do_lower_case": true,
+  "eos_token": "</s>",
+  "extra_special_tokens": {},
+  "mask_token": "<mask>",
+  "max_length": 128,
+  "model_max_length": 384,
+  "pad_to_multiple_of": null,
+  "pad_token": "<pad>",
+  "pad_token_type_id": 0,
+  "padding_side": "right",
+  "sep_token": "</s>",
+  "stride": 0,
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "MPNetTokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "[UNK]"
+}

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "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
+}

2_Dense/config.json

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+{"in_features": 768, "out_features": 512, "bias": true, "activation_function": "torch.nn.modules.activation.Tanh"}

2_Dense/model.safetensors

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+size 1575072

README.md

@@ -0,0 +1,555 @@
+---
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:3820
+- loss:MultipleNegativesRankingLoss
+widget:
+- source_sentence: samsung ms23h3125ak/ms23h3125ak
+  sentences:
+  - Canon EOS M50 + 15-45mm IS STM
+  - Bosch KIV32X23GB Integrated
+  - Indesit DIF04B1 Integrated
+  - Samsung MS23H3125AK Black
+  - Samsung RB29FWRNDBC Black
+  - Hisense RQ560N4WC1
+  - Samsung UE32M5520
+  - Nikon CoolPix A10
+  - Hotpoint RPD10457JKK
+  - HP Intel Xeon X5670 2.93GHz Socket 1366 3200MHz bus Upgrade Tray
+  - Indesit DFG15B1S Silver
+  - Samsung WW10M86DQOO
+  - Bosch SMV46MX00G Integrated
+  - LG 49SK8100PLA
+  - Nikon CoolPix W300
+  - AMD Ryzen 3 1300X 3.5GHz  Box
+  - LG OLED65B8PLA
+  - Samsung Galaxy J5 SM-J530
+  - LG 65UK6500PLA
+  - Siemens WM14T391GB
+  - Apple iPhone SE 32GB
+- source_sentence: lg oled65c8pla
+  sentences:
+  - Beko LCSM1545W White
+  - Bosch KAN90VI20G Stainless Steel
+  - Canon PowerShot SX60 HS
+  - Hotpoint WMAQF621P
+  - Apple iPhone 7 Plus 32GB
+  - Hotpoint FFU4DK Black
+  - Fujifilm Finepix XP130
+  - Bosch WAN24108GB
+  - LG OLED65E8PLA
+  - Intel Core i7-8700K 3.7GHz  Box
+  - Fujifilm X-Pro2
+  - LG OLED65C8PLA
+  - Samsung UE55NU8000
+  - LG 49LK5900PLA
+  - Apple iPhone 8 64GB
+  - Samsung UE65NU7100
+  - AEG L6FBG942R
+  - AMD Ryzen 7 1700 3GHz Box
+  - Panasonic TX-49FX750B
+  - Bosch WKD28351GB
+  - Bosch GUD15A50GB Integrated
+- source_sentence: 15.748 cm 6.2 2960 x 1440 samoled octa core 2.3ghz quad 1.7gh
+  sentences:
+  - Apple iPhone SE 32GB
+  - Apple iPhone X 64GB
+  - LG 55SK9500PLA
+  - Sony Cyber-shot DSC-WX500
+  - Samsung Galaxy A5 SM-A520F
+  - Apple iPhone 8 Plus 64GB
+  - Indesit IWDD7123
+  - Bosch SMS67MW01G White
+  - Bosch KGV33XW30G White
+  - Samsung WW80K5413UW
+  - AMD Ryzen 3 1300X 3.5GHz  Box
+  - Bosch WAW28750GB
+  - Samsung Galaxy S8+ 64GB
+  - Bosch KGN39VW35G White
+  - Intel Core i7-7700K 4.2GHz  Box
+  - Hotpoint RZAAV22P White
+  - Samsung UE49NU8000
+  - HP AMD Opteron 6276 2.3GHz Upgrade Tray
+  - Praktica Luxmedia Z250
+  - Hotpoint HFC2B19SV White
+  - Hisense RB385N4EW1 White
+- source_sentence: boxed processor amd ryzen 3 1200 4 x 3.1 ghz quad
+  sentences:
+  - Bosch KGN36HI32 Stainless Steel
+  - Bosch SMS24AW01G White
+  - Hotpoint WDAL8640P
+  - Doro 6050
+  - Samsung QE55Q7FN
+  - AMD Ryzen 3 1200 3.1GHz Box
+  - Samsung UE55NU7500
+  - Huawei Honor 10 128GB Dual SIM
+  - Sony Xperia L1
+  - Hotpoint FFU4DK Black
+  - Hoover DXOC 68C3B
+  - Sony Xperia XA1
+  - Nikon D7200 + 18-105mm VR
+  - HP Intel Xeon DP E5640 2.66GHz Socket 1366 1066MHz bus Upgrade Tray
+  - Samsung UE49NU8000
+  - Panasonic Lumix DMC-FT30
+  - Hotpoint FDL 9640K UK
+  - Apple iPhone 6S Plus 128GB
+  - Nikon D5600 + AF-P 18-55mm VR
+  - HP AMD Opteron 6238 2.6GHz Upgrade Tray
+  - Apple iPhone SE 32GB
+- source_sentence: lg 49uk6300plb/49uk6300plb
+  sentences:
+  - Bosch KIR24V20GB Integrated
+  - Bosch WAWH8660GB
+  - Intel Core i5-7600K 3.80GHz  Box
+  - Sony Bravia KD-65AF8
+  - Samsung RL4362FBASL Stainless Steel
+  - Bosch SMI50C15GB Silver
+  - Apple iPhone XS Max 256GB
+  - Fujifilm X-T100 + XC 15-45/f3.5-5.6 OIS PZ
+  - Bosch KGN36VW35G White
+  - Samsung WW70K5410UW
+  - Samsung Galaxy J6
+  - LG 49UK6300PLB
+  - Doro Secure 580
+  - Sony Xperia XZ1 Compact
+  - Bosch SMV50C10GB Integrated
+  - Bosch KGN34VB35G Black
+  - Panasonic NN-E27JWMBPQ White
+  - Samsung WW10M86DQOA/EU
+  - LG 55SK9500PLA
+  - Samsung QE65Q8DN
+  - Canon EOS 80D
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+metrics:
+- cosine_accuracy@1
+- cosine_accuracy@3
+- cosine_accuracy@5
+- cosine_accuracy@10
+- cosine_precision@1
+- cosine_precision@3
+- cosine_precision@5
+- cosine_precision@10
+- cosine_recall@1
+- cosine_recall@3
+- cosine_recall@5
+- cosine_recall@10
+- cosine_ndcg@10
+- cosine_mrr@10
+- cosine_map@100
+model-index:
+- name: SentenceTransformer
+  results:
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: Product Category Retrieval Test
+      type: Product-Category-Retrieval-Test
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.8085774058577406
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.9476987447698745
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.9644351464435147
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.9769874476987448
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.8085774058577406
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.3158995815899582
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.19288702928870294
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.09769874476987449
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.8085774058577406
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.9476987447698745
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.9644351464435147
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.9769874476987448
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.9041917131034228
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.879607906621505
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.8805000617705705
+      name: Cosine Map@100
+---
+
+# SentenceTransformer
+
+This is a [sentence-transformers](https://www.SBERT.net) model trained. It maps sentences & paragraphs to a 512-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
+
+## Model Details
+
+### Model Description
+- **Model Type:** Sentence Transformer
+<!-- - **Base model:** [Unknown](https://huggingface.co/unknown) -->
+- **Maximum Sequence Length:** 384 tokens
+- **Output Dimensionality:** 512 dimensions
+- **Similarity Function:** Cosine Similarity
+<!-- - **Training Dataset:** Unknown -->
+<!-- - **Language:** Unknown -->
+<!-- - **License:** Unknown -->
+
+### Model Sources
+
+- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
+- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
+- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
+
+### Full Model Architecture
+
+```
+SentenceTransformer(
+  (0): SentenceTransformer(
+    (0): Transformer({'max_seq_length': 384, 'do_lower_case': False}) with Transformer model: MPNetModel 
+    (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})
+    (2): Normalize()
+  )
+  (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})
+  (2): Dense({'in_features': 768, 'out_features': 512, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'})
+)
+```
+
+## Usage
+
+### Direct Usage (Sentence Transformers)
+
+First install the Sentence Transformers library:
+
+```bash
+pip install -U sentence-transformers
+```
+
+Then you can load this model and run inference.
+```python
+from sentence_transformers import SentenceTransformer
+
+# Download from the 🤗 Hub
+model = SentenceTransformer("llmvetter/embedding_finetune")
+# Run inference
+sentences = [
+    'lg 49uk6300plb/49uk6300plb',
+    'LG 49UK6300PLB',
+    'Samsung Galaxy J6',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 512]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(embeddings, embeddings)
+print(similarities.shape)
+# [3, 3]
+```
+
+<!--
+### Direct Usage (Transformers)
+
+<details><summary>Click to see the direct usage in Transformers</summary>
+
+</details>
+-->
+
+<!--
+### Downstream Usage (Sentence Transformers)
+
+You can finetune this model on your own dataset.
+
+<details><summary>Click to expand</summary>
+
+</details>
+-->
+
+<!--
+### Out-of-Scope Use
+
+*List how the model may foreseeably be misused and address what users ought not to do with the model.*
+-->
+
+## Evaluation
+
+### Metrics
+
+#### Information Retrieval
+
+* Dataset: `Product-Category-Retrieval-Test`
+* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| cosine_accuracy@1   | 0.8086     |
+| cosine_accuracy@3   | 0.9477     |
+| cosine_accuracy@5   | 0.9644     |
+| cosine_accuracy@10  | 0.977      |
+| cosine_precision@1  | 0.8086     |
+| cosine_precision@3  | 0.3159     |
+| cosine_precision@5  | 0.1929     |
+| cosine_precision@10 | 0.0977     |
+| cosine_recall@1     | 0.8086     |
+| cosine_recall@3     | 0.9477     |
+| cosine_recall@5     | 0.9644     |
+| cosine_recall@10    | 0.977      |
+| **cosine_ndcg@10**  | **0.9042** |
+| cosine_mrr@10       | 0.8796     |
+| cosine_map@100      | 0.8805     |
+
+<!--
+## Bias, Risks and Limitations
+
+*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
+-->
+
+<!--
+### Recommendations
+
+*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
+-->
+
+## Training Details
+
+### Training Dataset
+
+#### Unnamed Dataset
+
+
+* Size: 3,820 training samples
+* Columns: <code>sentence_0</code>, <code>sentence_1</code>, <code>sentence_2</code>, <code>sentence_3</code>, <code>sentence_4</code>, <code>sentence_5</code>, <code>sentence_6</code>, <code>sentence_7</code>, <code>sentence_8</code>, <code>sentence_9</code>, <code>sentence_10</code>, <code>sentence_11</code>, <code>sentence_12</code>, <code>sentence_13</code>, <code>sentence_14</code>, <code>sentence_15</code>, <code>sentence_16</code>, <code>sentence_17</code>, <code>sentence_18</code>, <code>sentence_19</code>, <code>sentence_20</code>, and <code>sentence_21</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | sentence_0                                                                        | sentence_1                                                                        | sentence_2                                                                        | sentence_3                                                                        | sentence_4                                                                        | sentence_5                                                                        | sentence_6                                                                        | sentence_7                                                                        | sentence_8                                                                        | sentence_9                                                                        | sentence_10                                                                       | sentence_11                                                                       | sentence_12                                                                       | sentence_13                                                                       | sentence_14                                                                       | sentence_15                                                                       | sentence_16                                                                       | sentence_17                                                                       | sentence_18                                                                       | sentence_19                                                                       | sentence_20                                                                       | sentence_21                                                                       |
+  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
+  | type    | string                                                                            | string                                                                            | string                                                                            | string                                                                            | string                                                                            | string                                                                            | string                                                                            | string                                                                            | string                                                                            | string                                                                            | string                                                                            | string                                                                            | string                                                                            | string                                                                            | string                                                                            | string                                                                            | string                                                                            | string                                                                            | string                                                                            | string                                                                            | string                                                                            | string                                                                            |
+  | details | <ul><li>min: 4 tokens</li><li>mean: 18.41 tokens</li><li>max: 47 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 10.94 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 11.11 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 11.15 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 10.89 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 10.89 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 10.98 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 11.07 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 11.04 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 10.84 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 10.82 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 10.81 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 11.05 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 10.92 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 11.18 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 11.07 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 10.93 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 11.02 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 11.04 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 11.02 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 10.95 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 10.86 tokens</li><li>max: 30 tokens</li></ul> |
+* Samples:
+  | sentence_0                                                           | sentence_1                              | sentence_2                                   | sentence_3                           | sentence_4                           | sentence_5                            | sentence_6                                    | sentence_7                        | sentence_8                       | sentence_9                                    | sentence_10                                                                  | sentence_11                                  | sentence_12                         | sentence_13                                 | sentence_14                                  | sentence_15                             | sentence_16                                      | sentence_17                    | sentence_18                               | sentence_19                            | sentence_20                                                                     | sentence_21                                                                     |
+  |:---------------------------------------------------------------------|:----------------------------------------|:---------------------------------------------|:-------------------------------------|:-------------------------------------|:--------------------------------------|:----------------------------------------------|:----------------------------------|:---------------------------------|:----------------------------------------------|:-----------------------------------------------------------------------------|:---------------------------------------------|:------------------------------------|:--------------------------------------------|:---------------------------------------------|:----------------------------------------|:-------------------------------------------------|:-------------------------------|:------------------------------------------|:---------------------------------------|:--------------------------------------------------------------------------------|:--------------------------------------------------------------------------------|
+  | <code>sony kd49xf8505bu 49 4k ultra hd tv</code>                     | <code>Sony Bravia KD-49XF8505</code>    | <code>Intel Core i7-8700K 3.7GHz  Box</code> | <code>Bosch WAN24100GB</code>        | <code>AMD FX-6300 3.5GHz  Box</code> | <code>Bosch WIW28500GB</code>         | <code>Bosch KGN36VL35G Stainless Steel</code> | <code>Indesit XWDE751480XS</code> | <code>CAT S41 Dual SIM</code>    | <code>Sony Xperia XA1 Ultra 32GB</code>       | <code>Samsung Galaxy J6</code>                                               | <code>Samsung QE55Q7FN</code>                | <code>Bosch KGN39VW35G White</code> | <code>Intel Core i5 7400 3.0GHz  Box</code> | <code>Neff C17UR02N0B Stainless Steel</code> | <code>Samsung RR39M7340SA Silver</code> | <code>Samsung RB41J7255SR Stainless Steel</code> | <code>Hoover DXOC 68C3B</code> | <code>Canon PowerShot SX730 HS</code>     | <code>Samsung RR39M7340BC Black</code> | <code>Praktica Luxmedia WP240</code>                                            | <code>HP Intel Xeon DP E5506 2.13GHz Socket 1366 800MHz bus Upgrade Tray</code> |
+  | <code>doro 8040 4g sim free mobile phone black</code>                | <code>Doro 8040</code>                  | <code>Bosch HMT75M551 Stainless Steel</code> | <code>Bosch SMI50C15GB Silver</code> | <code>Samsung WW90K5413UX</code>     | <code>Panasonic Lumix DMC-TZ70</code> | <code>Sony KD-49XF7073</code>                 | <code>Nikon CoolPix W100</code>   | <code>Samsung WD90J6A10AW</code> | <code>Bosch CFA634GS1B Stainless Steel</code> | <code>HP AMD Opteron 8425 HE 2.1GHz Socket F 4800MHz bus Upgrade Tray</code> | <code>Canon EOS 800D + 18-55mm IS STM</code> | <code>Samsung UE50NU7400</code>     | <code>Apple iPhone 6S 128GB</code>          | <code>Samsung RS52N3313SA/EU Graphite</code> | <code>Bosch WAW325H0GB</code>           | <code>Sony Bravia KD-55AF8</code>                | <code>Sony Alpha 6500</code>   | <code>Doro 5030</code>                    | <code>LG GSL761WBXV Black</code>       | <code>Bosch SMS67MW00G White</code>                                             | <code>AEG L6FBG942R</code>                                                      |
+  | <code>fridgemaster muz4965 undercounter freezer white a rated</code> | <code>Fridgemaster MUZ4965 White</code> | <code>Samsung UE49NU7100</code>              | <code>Nikon CoolPix A10</code>       | <code>Samsung UE55NU7100</code>      | <code>Samsung QE55Q7FN</code>         | <code>Bosch KGN49XL30G Stainless Steel</code> | <code>Samsung UE49NU7500</code>   | <code>LG 55UK6300PLB</code>      | <code>Hoover DXOC 68C3B</code>                | <code>Panasonic Lumix DMC-FZ2000</code>                                      | <code>Panasonic Lumix DMC-TZ80</code>        | <code>Bosch WKD28541GB</code>       | <code>Apple iPhone 6 32GB</code>            | <code>Sony Bravia KDL-32WE613</code>         | <code>Lec TF50152W White</code>         | <code>Bosch KGV36VW32G White</code>              | <code>Bosch WAYH8790GB</code>  | <code>Samsung RS68N8240B1/EU Black</code> | <code>Sony Xperia XZ1</code>           | <code>HP Intel Xeon DP E5506 2.13GHz Socket 1366 800MHz bus Upgrade Tray</code> | <code>Sharp R372WM White</code>                                                 |
+* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "cos_sim"
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `per_device_train_batch_size`: 32
+- `per_device_eval_batch_size`: 32
+- `num_train_epochs`: 8
+- `multi_dataset_batch_sampler`: round_robin
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: no
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 32
+- `per_device_eval_batch_size`: 32
+- `per_gpu_train_batch_size`: None
+- `per_gpu_eval_batch_size`: None
+- `gradient_accumulation_steps`: 1
+- `eval_accumulation_steps`: None
+- `torch_empty_cache_steps`: None
+- `learning_rate`: 5e-05
+- `weight_decay`: 0.0
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `max_grad_norm`: 1
+- `num_train_epochs`: 8
+- `max_steps`: -1
+- `lr_scheduler_type`: linear
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.0
+- `warmup_steps`: 0
+- `log_level`: passive
+- `log_level_replica`: warning
+- `log_on_each_node`: True
+- `logging_nan_inf_filter`: True
+- `save_safetensors`: True
+- `save_on_each_node`: False
+- `save_only_model`: False
+- `restore_callback_states_from_checkpoint`: False
+- `no_cuda`: False
+- `use_cpu`: False
+- `use_mps_device`: False
+- `seed`: 42
+- `data_seed`: None
+- `jit_mode_eval`: False
+- `use_ipex`: False
+- `bf16`: False
+- `fp16`: False
+- `fp16_opt_level`: O1
+- `half_precision_backend`: auto
+- `bf16_full_eval`: False
+- `fp16_full_eval`: False
+- `tf32`: None
+- `local_rank`: 0
+- `ddp_backend`: None
+- `tpu_num_cores`: None
+- `tpu_metrics_debug`: False
+- `debug`: []
+- `dataloader_drop_last`: False
+- `dataloader_num_workers`: 0
+- `dataloader_prefetch_factor`: None
+- `past_index`: -1
+- `disable_tqdm`: False
+- `remove_unused_columns`: True
+- `label_names`: None
+- `load_best_model_at_end`: False
+- `ignore_data_skip`: False
+- `fsdp`: []
+- `fsdp_min_num_params`: 0
+- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
+- `fsdp_transformer_layer_cls_to_wrap`: None
+- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
+- `deepspeed`: None
+- `label_smoothing_factor`: 0.0
+- `optim`: adamw_torch
+- `optim_args`: None
+- `adafactor`: False
+- `group_by_length`: False
+- `length_column_name`: length
+- `ddp_find_unused_parameters`: None
+- `ddp_bucket_cap_mb`: None
+- `ddp_broadcast_buffers`: False
+- `dataloader_pin_memory`: True
+- `dataloader_persistent_workers`: False
+- `skip_memory_metrics`: True
+- `use_legacy_prediction_loop`: False
+- `push_to_hub`: False
+- `resume_from_checkpoint`: None
+- `hub_model_id`: None
+- `hub_strategy`: every_save
+- `hub_private_repo`: None
+- `hub_always_push`: False
+- `gradient_checkpointing`: False
+- `gradient_checkpointing_kwargs`: None
+- `include_inputs_for_metrics`: False
+- `include_for_metrics`: []
+- `eval_do_concat_batches`: True
+- `fp16_backend`: auto
+- `push_to_hub_model_id`: None
+- `push_to_hub_organization`: None
+- `mp_parameters`: 
+- `auto_find_batch_size`: False
+- `full_determinism`: False
+- `torchdynamo`: None
+- `ray_scope`: last
+- `ddp_timeout`: 1800
+- `torch_compile`: False
+- `torch_compile_backend`: None
+- `torch_compile_mode`: None
+- `dispatch_batches`: None
+- `split_batches`: None
+- `include_tokens_per_second`: False
+- `include_num_input_tokens_seen`: False
+- `neftune_noise_alpha`: None
+- `optim_target_modules`: None
+- `batch_eval_metrics`: False
+- `eval_on_start`: False
+- `use_liger_kernel`: False
+- `eval_use_gather_object`: False
+- `average_tokens_across_devices`: False
+- `prompts`: None
+- `batch_sampler`: batch_sampler
+- `multi_dataset_batch_sampler`: round_robin
+
+</details>
+
+### Training Logs
+| Epoch  | Step | Training Loss | Product-Category-Retrieval-Test_cosine_ndcg@10 |
+|:------:|:----:|:-------------:|:----------------------------------------------:|
+| 1.0    | 120  | -             | 0.7406                                         |
+| 2.0    | 240  | -             | 0.8437                                         |
+| 3.0    | 360  | -             | 0.8756                                         |
+| 4.0    | 480  | -             | 0.8875                                         |
+| 4.1667 | 500  | 2.5302        | -                                              |
+| 5.0    | 600  | -             | 0.8963                                         |
+| 6.0    | 720  | -             | 0.9015                                         |
+| 7.0    | 840  | -             | 0.9042                                         |
+
+
+### Framework Versions
+- Python: 3.11.10
+- Sentence Transformers: 3.3.1
+- Transformers: 4.47.1
+- PyTorch: 2.5.1+cu124
+- Accelerate: 1.2.1
+- Datasets: 3.2.0
+- Tokenizers: 0.21.0
+
+## Citation
+
+### BibTeX
+
+#### Sentence Transformers
+```bibtex
+@inproceedings{reimers-2019-sentence-bert,
+    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
+    author = "Reimers, Nils and Gurevych, Iryna",
+    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
+    month = "11",
+    year = "2019",
+    publisher = "Association for Computational Linguistics",
+    url = "https://arxiv.org/abs/1908.10084",
+}
+```
+
+#### MultipleNegativesRankingLoss
+```bibtex
+@misc{henderson2017efficient,
+    title={Efficient Natural Language Response Suggestion for Smart Reply},
+    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
+    year={2017},
+    eprint={1705.00652},
+    archivePrefix={arXiv},
+    primaryClass={cs.CL}
+}
+```
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.3.1",
+    "transformers": "4.47.1",
+    "pytorch": "2.5.1+cu124"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

modules.json

@@ -0,0 +1,20 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "0_SentenceTransformer",
+    "type": "sentence_transformers.SentenceTransformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  },
+  {
+    "idx": 2,
+    "name": "2",
+    "path": "2_Dense",
+    "type": "sentence_transformers.models.Dense"
+  }
+]