initial commit

.gitattributes

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+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+vocab.txt filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

1_Pooling/config.json

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+{
+  "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
+}

README.md

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+---
+pipeline_tag: sentence-similarity
+tags:
+- sentence-transformers
+- feature-extraction
+- sentence-similarity
+
+---
+
+# nixie-suggest-small-v1
+
+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.
+
+This model is based on E5-small-v2 model, fine-tuned for typical suggester-like workloads:
+* for a partial and noisy input of the query, it tries to minimize the cosine distance to the correct query
+* 'mil' should be close to 'milk'
+* model also prone to typical typos like letter drops/swaps/duplications. So 'mikl' is still close to 'milk'.
+* the model is asymmetrical (as the original E5), so you need to prepend your prefixes with 'query: ' and full queries with 'passage: '
+
+## 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 = ["query: mil", "passage: milk"]
+
+model = SentenceTransformer('nixiesearch/nixie-suggest-small-v1')
+embeddings = model.encode(sentences)
+print(embeddings)
+```
+
+## Training dataset
+
+The training dataset was syntetically generated from the following corpora:
+* top-100k most frequent English words, from Google N-Gram project: [https://github.com/hackerb9/gwordlist](https://github.com/hackerb9/gwordlist)
+* top-1M 2-grams and 3-grams from [MultiLex](https://analytics.huma-num.fr/popr-ngram/Multi-LEX/index.html#en-section)
+
+We did the following permutations to the original 1/2/3-grams:
+* letter swaps: milk-mikl
+* letter drops: milk-ilk
+* qwerty-aware replacements: milk-nilk
+* duplications: milk-miilk
+
+The original generation code is available on github: https://github.com/nixiesearch/autocomplete-playground
+
+## Training
+The model was trained with the parameters:
+
+**DataLoader**:
+
+`torch.utils.data.dataloader.DataLoader` of length 220359 with parameters:
+```
+{'batch_size': 2048, 'sampler': 'torch.utils.data.dataloader._InfiniteConstantSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
+```
+
+**Loss**:
+
+`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
+  ```
+  {'scale': 20.0, 'similarity_fct': 'cos_sim'}
+  ```
+
+Parameters of the fit()-Method:
+```
+{
+    "epochs": 1,
+    "evaluation_steps": 3000,
+    "evaluator": "sentence_transformers.evaluation.RerankingEvaluator.RerankingEvaluator",
+    "max_grad_norm": 1,
+    "optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
+    "optimizer_params": {
+        "lr": 2e-05
+    },
+    "scheduler": "WarmupLinear",
+    "steps_per_epoch": 220358,
+    "warmup_steps": 1000,
+    "weight_decay": 0.01
+}
+```
+
+
+## Full Model Architecture
+```
+SentenceTransformer(
+  (0): Transformer({'max_seq_length': 512, '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})
+  (2): Normalize()
+)
+```
+
+## Citing & Authors
+
+<!--- Describe where people can find more information -->

config.json

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+{
+  "_name_or_path": "/home/shutty/.cache/torch/sentence_transformers/intfloat_e5-small-v2/",
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 384,
+  "initializer_range": 0.02,
+  "intermediate_size": 1536,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.31.0",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

config_sentence_transformers.json

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+{
+  "__version__": {
+    "sentence_transformers": "2.2.2",
+    "transformers": "4.31.0",
+    "pytorch": "2.0.1+cu117"
+  }
+}

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"
+  }
+]

onnx_convert.py

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+from transformers import AutoTokenizer, AutoModel
+import torch
+
+max_seq_length=128
+
+model = AutoModel.from_pretrained(".")
+model.eval()
+
+inputs = {"input_ids": torch.ones(1, max_seq_length, dtype=torch.int64),
+        "attention_mask": torch.ones(1, max_seq_length, dtype=torch.int64),
+        "token_type_ids": torch.ones(1, max_seq_length, dtype=torch.int64)}
+
+symbolic_names = {0: 'batch_size', 1: 'max_seq_len'}
+
+torch.onnx.export(model, args=tuple(inputs.values()), f='pytorch_model.onnx', export_params=True, 
+    input_names=['input_ids', 'attention_mask', 'token_type_ids'], output_names=['last_hidden_state'],
+    dynamic_axes={'input_ids': symbolic_names, 'attention_mask': symbolic_names, 'token_type_ids': symbolic_names})
+

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sentence_bert_config.json

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+{
+  "max_seq_length": 512,
+  "do_lower_case": false
+}

special_tokens_map.json

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+{
+  "cls_token": "[CLS]",
+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
+}

tokenizer.json

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tokenizer_config.json

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+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "mask_token": "[MASK]",
+  "model_max_length": 1000000000000000019884624838656,
+  "never_split": null,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}

vocab.txt

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