Add new SentenceTransformer model.

0_WordEmbeddings/pytorch_model.bin

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0_WordEmbeddings/tokenize_fn.pkl

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0_WordEmbeddings/whitespacetokenizer_config.json

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"edvr", "song", "pop", "beat", "normality", "credibility", "crowdsourced", "gendered", "pronouns", "seq", "adapters", "segan", "arena", "panoramic", "protocol", "rlcard", "card", "essential", "terms", "centroids", "textcaps", "memorization", "infusing", "finetuning", "discretized", "combating", "promp", "some", "considerations", "unfolding", "central", "parity", "odyssey", "significance", "finnish", "epidemiological", "command", "inputs", "dirichlet", "blocks", "cylindrical", "reprogramming", "ice", "mnasnet", "disguised", "stories", "tokenization", "spelling", "summarizing", "anywhere", "discriminating", "slate", "grasping", "tsetlin", "propositional", "clauses", "resynthesis", "diagnostics", "discover", "delete", "political", "fuse", "myocardial", "acceptable", "created", "abnormal", "aesthetics", "multigrid", "advertising", "lxmert", "mimicking", "modifications", "introspective", "typology", "dictionaries", "competitions", "11", "bitcoin", "metadata", "svms", "multilinear", "composable", "kronecker", "glove", "pacgan", "genre", "nuclear", "mixtures", "dof", "relocalization", "linknet", "indirect", "configuration", "richer", "artgan", "artwork", "polish", "abductive", "distilbert", "cheaper", "lighter", "fragments", "pirm", "books", "movies", "wrapper", "guiding", "sided", "tensornetwork", "hessian", "assignments", "15", "l2", "minimizing", "dutch", "encouraging", "fitting", "synergies", "sg", "useful", "used", "tough", "styled", "svd", "variants", "suspended", "dispersion", "unconditional", "rolling", "unrolling", "authorship", "championship", "manual", "negation", "share", "correlational", "authentication", "genres", "pc", "6dof", "ternary", "mentions", "communities", "infarction", "while", "nuclei", "demystifying", "obfuscation", "compute", "differences", "bits", "shrinking", "proofs", "facebook", "submission", "bleu", "robustifying", "corruptions", "competing", "risks", "romanian", "posts", "reconstructing", "undersampled", "take", "cr", "configurable", "vq", "grams", "annealing", "atlas", "surprising", "protocols", "stacks", "gains", "there", "deepwalk", "represent", "libraries", "cover", "cities", "humor", "literature", "index", "isolation", "corrupted", "meme", "recognize", "enforcing", "polysemous", "continually", "trec", "put", "prioritization", "fix", "phenomena", "centered", "reranking", "normals", "phoneme", "reset", "poker", "probabilities", "branched", "simplex", "additional", "pvanet", "subtraction", "interleaved", "optimistic", "conjunctive", "timed", "increased", "hyperband", "verb", "conditionally", "extrapolation", "implementing", "spike", "perform", "analogy", "concentration", "travelling", "salesman", "normalising", "comprehend", "lectures", "re3", "fastmri", "atrial", "superpixels", "annular", "netvlad", "hosted", "isic", "poly", "avoiding", "known", "stem", "thorough", "daily", "sphereface", "multiobjective", "p", "costly", "occurrence", "rezero", "noun", "lens", "discretization", "supertagging", "xlnet", "seesawfacenets", "connectionist", "20", "forcing", "pcrnet", "decompositions", "es", "fixations", "plastic", "relaxed", "limit", "marco", "performing", "outliers", "being", "dilemmas", "syntactically", "unfairness", "trackers", "infilling", "prover", "affordance", "omics", "typed", "svdd", "tumour", "crosslingual", "rescaling", "prices", "zoom", "wifi", "biasing", "prototyping", "lost", "everyone", "revenge", "optimizations", "fairseq", "tests", "promise", "eunn", "hash", "return", "attending", "tcav", "pico", "pan", "isolating", "id", "acceptability", "analogies", "alleviating", "stylized", "distinct", "framing", "chains", "inherent", "retouching", "referential", "github", "emoji2vec", "electrocardiography", "wav2vec", "permuted", "flownet3d", "item2vec", "liquid", "plda", "musegan", "accompaniment", "salt", "ssim", "decipherment", "pointgoal", "intersectional", "winograd", "core", "thumbs", "lab", "undirected", "viewpoints", "ncrf", "cardinality", "stabilization", "children", "traveling", "autoencoded", "jiant", "controllers", "inferencing", "outlook", "genomic", "struc2vec", "postprocessing", "spice", "grade", "foveation", "activated", "edinburgh", "terminology", "remove", "shadows", "ghost", "pointhop", "highlight", "snes", "microphone", "captured", "stitching", "gpr", "disfluency", "epidemiology", "lipschitzlr", "board", "coresets", "transliteration", "execute", "centermask", "unity", "demosaicking", "mcqa", "bsds500", "smac", "montezuma", "pepper", "humanitarian", "deception", "seperation", "deblocking", "overlapped", "bitext", "acrobot", "100k"], "stop_words": [], "do_lower_case": true}

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

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+---
+pipeline_tag: sentence-similarity
+tags:
+- sentence-transformers
+- feature-extraction
+- sentence-similarity
+
+---
+
+# lambdaofgod/query-titles_dependencies-nbow-nbow-mnrl
+
+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-titles_dependencies-nbow-nbow-mnrl')
+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-titles_dependencies-nbow-nbow-mnrl)
+
+
+
+## Full Model Architecture
+```
+SentenceTransformer(
+  (0): WordEmbeddings(
+    (emb_layer): Embedding(4395, 200)
+  )
+  (1): WordWeights(
+    (emb_layer): Embedding(4395, 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": "4.20.0",
+    "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"
+  }
+]