初始化项目，由ModelHub XC社区提供模型

Model: good-ai-club/NBB
Source: Original Platform

.gitattributes

@@ -0,0 +1,27 @@
+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zstandard filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text

1_Pooling/config.json

@@ -0,0 +1,7 @@
+{
+  "word_embedding_dimension": 768,
+  "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 +1,125 @@
+---
+pipeline_tag: sentence-similarity
+tags:
+- sentence-transformers
+- feature-extraction
+- sentence-similarity
+- transformers
+---
+
+# {MODEL_NAME}
+
+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.
+
+<!--- 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('{MODEL_NAME}')
+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
+
+
+#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('{MODEL_NAME}')
+model = AutoModel.from_pretrained('{MODEL_NAME}')
+
+# 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. In this case, mean pooling.
+sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
+
+print("Sentence embeddings:")
+print(sentence_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={MODEL_NAME})
+
+
+## Training
+The model was trained with the parameters:
+
+**DataLoader**:
+
+`torch.utils.data.dataloader.DataLoader` of length 3188 with parameters:
+```
+{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
+```
+
+**Loss**:
+
+`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss` 
+
+Parameters of the fit()-Method:
+```
+{
+    "epochs": 5,
+    "evaluation_steps": 355,
+    "evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
+    "max_grad_norm": 1,
+    "optimizer_class": "<class 'transformers.optimization.AdamW'>",
+    "optimizer_params": {
+        "lr": 2e-05
+    },
+    "scheduler": "WarmupLinear",
+    "steps_per_epoch": null,
+    "warmup_steps": 1594,
+    "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': 768, '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.json

@@ -0,0 +1,26 @@
+{
+  "_name_or_path": "model_data/negbiobert/",
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "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": "4.15.0",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 28996
+}

config_sentence_transformers.json

@@ -0,0 +1,7 @@
+{
+  "__version__": {
+    "sentence_transformers": "2.1.0",
+    "transformers": "4.15.0",
+    "pytorch": "1.10.1+cu102"
+  }
+}

eval/similarity_evaluation_test_results.csv

@@ -0,0 +1,62 @@
+epoch,steps,cosine_pearson,cosine_spearman,euclidean_pearson,euclidean_spearman,manhattan_pearson,manhattan_spearman,dot_pearson,dot_spearman
+0,355,0.5583366894931981,0.5879171457264535,0.6187228035582647,0.5960684291388293,0.6209547284361288,0.598161686529525,0.5605586812719913,0.5765079824234417
+0,710,0.814789755750387,0.7952329696687719,0.8427306509253722,0.7951945084266873,0.8429860371854735,0.7944472289085838,0.8137734764741484,0.7925630073529761
+0,1065,0.8601478906368689,0.8502536011267224,0.8677206348958932,0.8468601907364758,0.8670996418710004,0.8439405163911546,0.8595531882570833,0.8477452808832497
+0,1420,0.8897907718256918,0.8896228159233622,0.8895600304717801,0.8842852573542318,0.8881327394622001,0.8782665485806255,0.8889850224003417,0.8850856651356128
+0,1775,0.9065726666937967,0.8969793605424452,0.8966345452519291,0.8894135244684391,0.8946342911615138,0.884409581409222,0.9038213345576037,0.8914014494628125
+0,2130,0.9117361342124195,0.8990334974184645,0.902806613452293,0.8927821057615118,0.9010190376183855,0.8886955172139939,0.9097857102013863,0.8938156045555407
+0,2485,0.9248252751841822,0.9046015168752581,0.9114634130088588,0.9008984083156336,0.9100354159816207,0.8982788335010796,0.9227149782552097,0.9000055899052011
+0,2840,0.9216924753356608,0.9032251329619665,0.9086717248930668,0.9004584504373058,0.9078365442886039,0.8981115133707428,0.9203591277404498,0.8991820055204256
+0,-1,0.9294272069813354,0.9070461767611926,0.9135173566708763,0.9034696949109786,0.9127342624787116,0.9019091939599311,0.9284588841912416,0.9043518401633839
+1,355,0.9347104854233528,0.9103188516418075,0.9178172543947876,0.9067658328102401,0.9169403320011045,0.9046974698313819,0.9332283804181861,0.9054252673424329
+1,710,0.9358837992088574,0.9114909294463441,0.9199105165571793,0.9088674318239877,0.9193868435419604,0.9074220966452082,0.934698235492454,0.908456047204866
+1,1065,0.9389151039780615,0.9121177531346226,0.9207260416846682,0.9104825836560623,0.9202248333712243,0.9095908821132189,0.9374035482964748,0.9096480685485159
+1,1420,0.9455436041007601,0.9168209882584631,0.9276233616834709,0.9151665777027929,0.9272640365309093,0.9143808013386198,0.944646818823039,0.9152520740392804
+1,1775,0.9453875738873628,0.9157874034381355,0.9264486700912917,0.9132700290004935,0.9258078418314356,0.9114939922410135,0.9440308509281532,0.9124613261650951
+1,2130,0.9465193346456511,0.9174864954824391,0.9293857682699497,0.9150580092645108,0.929030891668863,0.9134669568624789,0.9456879515406806,0.9149568371900135
+1,2485,0.9487366406980677,0.9167874303508584,0.9306433954550803,0.9150139219891529,0.9302684186655796,0.9133864775152748,0.9480502524517572,0.9146857577154789
+1,2840,0.9518258923785943,0.9205472864773787,0.9333049180540749,0.9182452149868892,0.93292884475146,0.9171070535000744,0.9510502411530556,0.9180957149652748
+1,-1,0.9539887698552225,0.9224583181283939,0.9341618974208978,0.919954341719721,0.933730648406728,0.9184093568097003,0.9533286682123181,0.9206538466695796
+2,355,0.955324099144229,0.922695412772351,0.9365832666573926,0.920425834674596,0.9361193257706135,0.9191137407012675,0.9546519778199754,0.9205911584846025
+2,710,0.9547230367902307,0.921741513279418,0.9355327716771122,0.9197713397267115,0.9351816090063717,0.9185614201873189,0.9540460726032891,0.9195629248270117
+2,1065,0.9540473935308138,0.9217706831850144,0.935350891379822,0.9190899123858166,0.935072252239686,0.9179726927456412,0.9534029564405359,0.9201026755352946
+2,1420,0.9566058136633234,0.92398164920871,0.9378832239072554,0.9219724620771569,0.9374838875986832,0.9211660514424304,0.9560243081195118,0.9225500435855684
+2,1775,0.95584668996074,0.9232107864059828,0.9371577994299786,0.9217490347089926,0.9370241395765756,0.9207141149518449,0.9555026556334517,0.9224760119665925
+2,2130,0.9587730663376958,0.9249458446864519,0.9390480007787791,0.9233303628195288,0.9388292439989915,0.922631492484566,0.9581225658931,0.9241911675955138
+2,2485,0.9586087137487693,0.9238832363735751,0.9394687631045276,0.9221467269033127,0.9390993294666172,0.9210556150078436,0.9579446679739171,0.9233739343947002
+2,2840,0.9594830685316189,0.9238792756195224,0.9401532436143242,0.9225412071212963,0.940226161118004,0.9222793570475284,0.9591611694592489,0.9232293128920515
+2,-1,0.9610816998887871,0.9263539408210805,0.9411500422711135,0.9246844516888534,0.9411594745775475,0.9241989412641242,0.9606794108101743,0.9252238375560139
+3,355,0.9638962526829634,0.9268487591265989,0.9437122800681477,0.9253208888313675,0.9436631481001093,0.9248107781158977,0.9633653491367968,0.9257145878133753
+3,710,0.9640071994377717,0.9274325399963707,0.9440418439308584,0.9253018951646795,0.9439568740634646,0.9250727383172144,0.9633307039540846,0.925861600931539
+3,1065,0.9652406893497275,0.9285917724728945,0.9450125347422205,0.9267269362200838,0.9448994304486278,0.9260807047683052,0.9647702034947601,0.9276689291019933
+3,1420,0.9662743351721511,0.9296478944876604,0.9459920553671606,0.9280785560887586,0.9460151241458922,0.9273763040826746,0.9658216336323329,0.9288338801807885
+3,1775,0.9663593724143488,0.9290290018066383,0.945807606246542,0.9275751542631003,0.9458155741725812,0.9269933099620016,0.965960327795632,0.9285452214343378
+3,2130,0.966722703524383,0.9290490254448027,0.9460980687956181,0.9272413951277594,0.9460403227878083,0.9260969881827996,0.9663423767286617,0.9283070806156174
+3,2485,0.9687460491838675,0.9312713074772231,0.9483430760485022,0.9299038743944714,0.9483310592870006,0.9293301896535613,0.9684352270351246,0.9309801892734233
+3,2840,0.9694650595968591,0.9312770775900531,0.9484487627064206,0.9299710820099024,0.9484258726161442,0.9291835468844759,0.9691118568077712,0.9311688199688152
+3,-1,0.971046083727986,0.9324588754112599,0.9504447377218922,0.9309203799173904,0.9503996836541595,0.9304612813180594,0.9706866504403765,0.9323868573260796
+4,355,0.9716182572783036,0.9319985187596311,0.9506895610295362,0.9305972871535784,0.9506707851378341,0.9302168040905692,0.9713243160088493,0.9319639437980494
+4,710,0.9717784770520836,0.9325465505108973,0.9508240483784225,0.9308629880541769,0.9507582240372875,0.9304789117617296,0.9713933190122204,0.9322414835442265
+4,1065,0.9724183844767168,0.9325009394377651,0.9508810802208263,0.9313230195468041,0.9508767541937196,0.9308221749413169,0.9719769497303579,0.9324036355817955
+4,1420,0.9726713203839563,0.9337166653073762,0.9515327495994064,0.9323007837996008,0.9515645453887573,0.932004932800195,0.9723191641420994,0.9334167246481391
+4,1775,0.9727339120485562,0.9335610765577419,0.9518613876315792,0.9317843689925049,0.9518644902787655,0.931490898381524,0.9724030586309488,0.9332385116734251
+4,2130,0.9728508145984897,0.9332219559139694,0.9516485701201889,0.9319441050236982,0.9516282119327871,0.9312923585695627,0.9725508960138037,0.9332249167506295
+4,2485,0.9735166233420254,0.9336009166898012,0.9522669293196775,0.9323572674271123,0.9522416591275855,0.9316663906523233,0.9731527539856639,0.9336297843912685
+4,2840,0.9734989046495076,0.9337122939586225,0.9521763061406225,0.9324393394323598,0.9522017314099828,0.9317849628383919,0.9731794173431918,0.9336891450836925
+4,-1,0.9736028744533611,0.9337770666025698,0.9522979858591861,0.9323910677249013,0.952311043088673,0.9317648540375867,0.9732812422229503,0.9337486787570943
+0,355,0.9909294759846752,0.9406461146552008,0.9654585882624435,0.9404797567214133,0.9654469984342662,0.9403433196424966,0.9903764423216391,0.9406121900292931
+0,710,0.9880007979364105,0.9394886099222778,0.9636521602072328,0.9393495689158023,0.9635992368354801,0.9392218386981466,0.9875295873970118,0.9393806466960986
+0,1065,0.9867325785849648,0.9389293398757758,0.9617385397368801,0.9387052137207775,0.9617878758906122,0.9384154653901385,0.986235327246042,0.9387732953280533
+0,1420,0.9830848782233071,0.9374171949457755,0.9585602242769784,0.9371737814439977,0.9585794745491611,0.9371685278166934,0.9828252327778624,0.9373252379128356
+0,1775,0.9828017847661067,0.9375572152364644,0.9584212594512916,0.9373450786295814,0.9584767950192508,0.9369686860967126,0.9824997936828636,0.9373923721914251
+0,2130,0.9833369349179267,0.9373208578197997,0.9593422166245292,0.9372360694280912,0.9593251863330297,0.9368732383428817,0.9829620569746693,0.9370269285892987
+0,2485,0.9809210060380957,0.9361673304384327,0.9564078452143101,0.9358385277755191,0.9564961986174151,0.9355276587745383,0.980485194610244,0.9358854886472471
+0,2840,0.9827698938535824,0.9374750412112209,0.9585475168239459,0.9371931702900773,0.9585221932226314,0.9368593170878714,0.9823418671952469,0.9373189147607697
+0,-1,0.9843839061050649,0.9377060517826009,0.9600452752319536,0.9371118406262705,0.9600640327848027,0.9368631928264805,0.9837934505550873,0.9375202584288171
+1,355,0.9838225314950014,0.9373059180333706,0.9598054144793794,0.9372103187466179,0.9598169128857215,0.937035957654217,0.9833738723208423,0.937429793501737
+1,710,0.9813374141258323,0.9356522091041934,0.9577248981258397,0.9355660792591983,0.9576645252413685,0.9352571517473162,0.9809957194389275,0.9354612168508111
+1,1065,0.9833693001876277,0.936879454851929,0.9595690539001053,0.9368708629777989,0.959667866033299,0.9366496756103173,0.9828714837460176,0.9369054134384645
+1,1420,0.9840634003922895,0.93769154544788,0.9605231677829276,0.9374677879455038,0.9603741203716987,0.9370609412553488,0.9834718770491541,0.9377023609482472
+1,1775,0.9824809737370099,0.9366572144108479,0.9593608674632799,0.9361388531178635,0.9595176205190066,0.9358299155091362,0.9820533323399783,0.9365056485523606
+1,2130,0.9830453415845913,0.9377029958355131,0.9598305268302101,0.937236789641698,0.9598485417769319,0.9366538829321416,0.9826349536274687,0.9374591546706882
+1,2485,0.9846919912172645,0.9385645153049571,0.9610526522631251,0.9381051003107669,0.9610272250271868,0.9376565826387366,0.9840897693730202,0.9383128193075232

modules.json

@@ -0,0 +1,14 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  }
+]

pytorch_model.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7e4b6f08cb1bfe6db06fd59dc8d8f6e2313aa524a905d30556953cc47e59ebe6
+size 433322417

sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+  "max_seq_length": 512,
+  "do_lower_case": false
+}

special_tokens_map.json

@@ -0,0 +1 @@
+{"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"}

tokenizer_config.json

@@ -0,0 +1 @@
+{"do_lower_case": false, "unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]", "tokenize_chinese_chars": true, "strip_accents": null, "model_max_length": 512, "special_tokens_map_file": "/home/studio-lab-user/.cache/huggingface/transformers/118da8438a7854000cfcf052566f83ae4f4159ac25796e49e16c3b18746041b4.dd8bd9bfd3664b530ea4e645105f557769387b3da9f79bdb55ed556bdd80611d", "name_or_path": "model_data/negbiobert/", "do_basic_tokenize": true, "never_split": null, "tokenizer_class": "BertTokenizer"}