beeformer
/

movielens-mpnet-base-v2

@@ -1,6 +1,6 @@
 ---
 language: en
-license: apache-2.0
 library_name: sentence-transformers
 tags:
 - sentence-transformers
@@ -9,33 +9,11 @@ tags:
 - transformers
 datasets:
 - beeformer/recsys-movielens-20m
-- 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:
@@ -47,132 +25,38 @@ 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** |

 ---
 language: en
+license: cc-by-nc-4.0
 library_name: sentence-transformers
 tags:
 - sentence-transformers
 - transformers
 datasets:
 - beeformer/recsys-movielens-20m
 pipeline_tag: sentence-similarity
 ---
+# movielens-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 it is designed to use in recommender systems for content-base filtering and as a side information for cold-start recommendation.
 ## Usage (Sentence-Transformers)
 Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
 Then you can use the model like this:
 ```python
 from sentence_transformers import SentenceTransformer
+sentences = ["This is an example product description", "Each product description is converted"]
+model = SentenceTransformer('beeformer/movielens-mpnet-base-v2')
 embeddings = model.encode(sentences)
 print(embeddings)
 ```
+## Training procedure
+### Pre-training
+We use the pretrained [`sentence-transformers/all-mpnet-base-v2`](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) model. Please refer to the model card for more detailed information about the pre-training procedure.
+### Fine-tuning
+ We use the initial model without modifying its architecture or pre-trained model parameters.
+ However, we reduce the processed sequence length to 256 to reduce the training time of the model.
+ Regarding other hyperparameters, we use the same interaction data batch size of 1024; we use the negative sampling parameter m = 10000.
+ We use constant learning rate of 1e-5, and we train the model for ten epochs.
+ We finetuned our model on the MovieLens-20M dataset. For details please see our paper (link TBA).
+ For item ids used during training please see (links TBA).
 ## Evaluation Results
+For ids of items used for coldstart evaluation please see (links TBA).
+Table with results TBA.
 ## Intended uses
+This model was trained as a demonstration of capabilities of the beeFormer training framework (link and details TBA) and is intended for research purposes only.
+## Citation
+TBA