Add new SentenceTransformer model.

.gitattributes

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0_WordEmbeddings/whitespacetokenizer_config.json

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"tract", "transferred", "transfers", "transformable", "treatments", "triggered", "tsdae", "tsp", "tube", "tubular", "tweening", "typologically", "ulmfit", "uncurated", "undersampling", "unification", "unintended", "unlocking", "unreal", "uplift", "utilization", "varied", "verbalization", "vi", "vinvl", "vl", "voicefilter", "voices", "voxelmorph", "voxels", "vr", "walking", "watershed", "ways", "wikimatrix", "winners", "workflows", "worrying", "wrapper", "xtreme", "zooming", "}$", "\u201c", "!:", "$^\\", ".'", "2003", "21", "30", "50", "6m", "?!", "?\"", "abd", "accuracies", "acts", "adaboost", "adamatch", "adamp", "adaptative", "addition", "aggression", "ais", "alfred", "aligner", "alive", "alphafold", "alphazero", "am", "analyses", "analyzers", "ancestry", "anchored", "animals", "annealed", "annular", "anode", "anonymized", "anywhere", "apache", "aperture", "apollo", "approaching", "around", "arousal", "artefact", "artists", "asap", "ascent", "asl", "assembled", "atlases", "atmospheric", "au", "audience", "audioset", "autoencoded", "autofocus", "autogan", "automunge", "avalanche", "aversion", "backpropagated", "bags", "baidu", "basket", "batched", "bert4rec", "berts", "bible", "bifurcated", "bins", "biomarker", "birth", "blessings", "blockmodels", "blogs", "blow", "boat", "bow", "bros", "brushstroke", "buffer", "bugs", "burns", "byt5", "caenorhabditis", "caire", "calculating", "calculus", "camel", "canine", "cardiovascular", "catalog", "catalyst", "cauchy", "century", "certificates", "chaining", "charting", "cholectriplet2021", "circ", "circular", "cityscapes", "cl", "clarification", "clarinet", "climategan", "clothes", "clotho", "clutrr", "cm", "coach", "cobra", "codebook", "coffee", "cola", "collapsed", "collected", "colon", "coming", "commercial", "communicative", "compartmental", "competence", "compliant", "complicated", "concatenated", "concatenation", "concentrated", "confined", "congestion", "conic", "consciousness", "consecutive", "consider", 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0_WordEmbeddings/wordembedding_config.json

@@ -0,0 +1,5 @@
+{
+  "tokenizer_class": "mlutil.sentence_transformers_utils.CustomTokenizer",
+  "update_embeddings": false,
+  "max_seq_length": 1000000
+}

2_Pooling/config.json

@@ -0,0 +1,7 @@
+{
+  "word_embedding_dimension": 200,
+  "pooling_mode_cls_token": false,
+  "pooling_mode_mean_tokens": true,
+  "pooling_mode_max_tokens": false,
+  "pooling_mode_mean_sqrt_len_tokens": false
+}

README.md

@@ -0,0 +1,60 @@
+---
+pipeline_tag: sentence-similarity
+tags:
+- sentence-transformers
+- feature-extraction
+- sentence-similarity
+
+---
+
+# lambdaofgod/query_nbow_embedder
+
+This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 200 dimensional dense vector space and can be used for tasks like clustering or semantic search.
+
+<!--- Describe your model here -->
+
+## 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('lambdaofgod/query_nbow_embedder')
+embeddings = model.encode(sentences)
+print(embeddings)
+```
+
+
+
+## Evaluation Results
+
+<!--- Describe how your model was evaluated -->
+
+For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=lambdaofgod/query_nbow_embedder)
+
+
+
+## Full Model Architecture
+```
+SentenceTransformer(
+  (0): WordEmbeddings(
+    (emb_layer): Embedding(6912, 200)
+  )
+  (1): WordWeights(
+    (emb_layer): Embedding(6912, 1)
+  )
+  (2): Pooling({'word_embedding_dimension': 200, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
+)
+```
+
+## Citing & Authors
+
+<!--- Describe where people can find more information -->

config_sentence_transformers.json

@@ -0,0 +1,7 @@
+{
+  "__version__": {
+    "sentence_transformers": "2.2.2",
+    "transformers": "4.10.1",
+    "pytorch": "1.10.0+cu111"
+  }
+}

modules.json

@@ -0,0 +1,20 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "0_WordEmbeddings",
+    "type": "sentence_transformers.models.WordEmbeddings"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_WordWeights",
+    "type": "sentence_transformers.models.WordWeights"
+  },
+  {
+    "idx": 2,
+    "name": "2",
+    "path": "2_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  }
+]