Upload multitask artifacts

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+encoder/tokenizer.json filter=lfs diff=lfs merge=lfs -text

encoder/README.md

@@ -1,5 +1,55 @@
 ---
-language: en
+language:
+- multilingual
+- ar
+- bg
+- ca
+- cs
+- da
+- de
+- el
+- en
+- es
+- et
+- fa
+- fi
+- fr
+- gl
+- gu
+- he
+- hi
+- hr
+- hu
+- hy
+- id
+- it
+- ja
+- ka
+- ko
+- ku
+- lt
+- lv
+- mk
+- mn
+- mr
+- ms
+- my
+- nb
+- nl
+- pl
+- pt
+- ro
+- ru
+- sk
+- sl
+- sq
+- sr
+- sv
+- th
+- tr
+- uk
+- ur
+- vi
 license: apache-2.0
 library_name: sentence-transformers
 tags:
@@ -7,36 +57,22 @@ tags:
 - 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
+language_bcp47:
+- fr-ca
+- pt-br
+- zh-cn
+- zh-tw
 pipeline_tag: sentence-similarity
 ---
 
+# sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
 
-# all-MiniLM-L6-v2
 This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 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:
 
 ```
@@ -44,24 +80,27 @@ 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-MiniLM-L6-v2')
+model = SentenceTransformer('sentence-transformers/paraphrase-multilingual-MiniLM-L12-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
+
+# 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()
@@ -72,8 +111,8 @@ def mean_pooling(model_output, attention_mask):
 sentences = ['This is an example sentence', 'Each sentence is converted']
 
 # Load model from HuggingFace Hub
-tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/all-MiniLM-L6-v2')
-model = AutoModel.from_pretrained('sentence-transformers/all-MiniLM-L6-v2')
+tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2')
+model = AutoModel.from_pretrained('sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2')
 
 # Tokenize sentences
 encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
@@ -82,92 +121,36 @@ encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tenso
 with torch.no_grad():
     model_output = model(**encoded_input)
 
-# Perform pooling
+# Perform pooling. In this case, max 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)
 ```
 
-------
-
-## 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 [`nreimers/MiniLM-L6-H384-uncased`](https://huggingface.co/nreimers/MiniLM-L6-H384-uncased) 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 developed 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 developed 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 intended to be used as a sentence and short paragraph encoder. Given an input text, it outputs 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 256 word pieces is truncated.
-
-
-## Training procedure
-
-### Pre-training 
-
-We use the pretrained [`nreimers/MiniLM-L6-H384-uncased`](https://huggingface.co/nreimers/MiniLM-L6-H384-uncased) 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 our 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** |
+
+
+## Full Model Architecture
+```
+SentenceTransformer(
+  (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel 
+  (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
+)
+```
+
+## Citing & Authors
+
+This model was trained by [sentence-transformers](https://www.sbert.net/). 
+        
+If you find this model helpful, feel free to cite our publication [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://arxiv.org/abs/1908.10084):
+```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 = "http://arxiv.org/abs/1908.10084",
+}
+```

encoder/adapter_config.json

@@ -6,7 +6,7 @@
     "base_model_class": "BertModel",
     "parent_library": "transformers.models.bert.modeling_bert"
   },
-  "base_model_name_or_path": "sentence-transformers/all-MiniLM-L6-v2",
+  "base_model_name_or_path": "sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2",
   "bias": "none",
   "corda_config": null,
   "ensure_weight_tying": false,

encoder/adapter_model.safetensors

@@ -1,3 +1,3 @@
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+oid sha256:b7f81d2c552aff5ed2d3ed2282d22b3746fbaad36e1bbcfe6062917b7335ea85
+size 596240

encoder/lora_weights/README.md

@@ -1,8 +1,8 @@
 ---
-base_model: sentence-transformers/all-MiniLM-L6-v2
+base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
 library_name: peft
 tags:
-- base_model:adapter:sentence-transformers/all-MiniLM-L6-v2
+- base_model:adapter:sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
 - lora
 - transformers
 ---

encoder/lora_weights/adapter_config.json

@@ -6,7 +6,7 @@
     "base_model_class": "BertModel",
     "parent_library": "transformers.models.bert.modeling_bert"
   },
-  "base_model_name_or_path": "sentence-transformers/all-MiniLM-L6-v2",
+  "base_model_name_or_path": "sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2",
   "bias": "none",
   "corda_config": null,
   "ensure_weight_tying": false,

encoder/lora_weights/adapter_model.safetensors

@@ -1,3 +1,3 @@
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+size 596240

encoder/modules.json

@@ -10,11 +10,5 @@
     "name": "1",
     "path": "1_Pooling",
     "type": "sentence_transformers.models.Pooling"
-  },
-  {
-    "idx": 2,
-    "name": "2",
-    "path": "2_Normalize",
-    "type": "sentence_transformers.models.Normalize"
   }
 ]

encoder/sentence_bert_config.json

@@ -1,4 +1,4 @@
 {
-    "max_seq_length": 256,
+    "max_seq_length": 128,
     "do_lower_case": false
 }

encoder/tokenizer_config.json

@@ -1,16 +1,23 @@
 {
   "backend": "tokenizers",
-  "cls_token": "[CLS]",
-  "do_basic_tokenize": true,
+  "bos_token": "<s>",
+  "cls_token": "<s>",
   "do_lower_case": true,
+  "eos_token": "</s>",
   "is_local": false,
-  "mask_token": "[MASK]",
-  "model_max_length": 256,
-  "never_split": null,
-  "pad_token": "[PAD]",
-  "sep_token": "[SEP]",
+  "mask_token": "<mask>",
+  "max_length": 128,
+  "model_max_length": 128,
+  "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": "BertTokenizer",
-  "unk_token": "[UNK]"
+  "tokenizer_class": "TokenizersBackend",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "<unk>"
 }

heads.pt

@@ -1,3 +1,3 @@
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+oid sha256:5b749e2efcbf0e9cf8ae6fc3904ed56a7e2f09ea5c6a81e0fec20fa79567317f
+size 897371

id2label.json

@@ -10,7 +10,6 @@
   },
   "context": {
     "0": "followup",
-    "1": "label",
-    "2": "standalone"
+    "1": "standalone"
   }
 }

label2id.json

@@ -10,7 +10,6 @@
   },
   "context": {
     "followup": 0,
-    "label": 1,
-    "standalone": 2
+    "standalone": 1
   }
 }

train_config.json

@@ -1,19 +1,21 @@
 {
-  "model_name": "sentence-transformers/all-MiniLM-L6-v2",
+  "model_name": "paraphrase-multilingual-MiniLM-L12-v2",
   "max_length": 128,
-  "epochs": 12,
+  "epochs": 20,
   "batch_size": 16,
-  "lr_encoder": 1e-05,
-  "lr_heads": 0.0005,
+  "lr_encoder": 8e-06,
+  "lr_heads": 0.0004,
   "weight_decay": 0.05,
   "task_weight_macro": 1.0,
-  "task_weight_intent": 1.8,
-  "task_weight_context": 0.7,
+  "task_weight_intent": 1.4,
+  "task_weight_context": 0.8,
   "device": "cuda",
   "output_dir": "models\\artifacts",
   "seed": 42,
   "use_lora": true,
   "lora_r": 8,
   "lora_alpha": 16,
-  "lora_dropout": 0.1
+  "lora_dropout": 0.1,
+  "patience": 5,
+  "min_delta": 0.001
 }