初始化项目，由ModelHub XC社区提供模型

Model: sentence-transformers/msmarco-MiniLM-L12-cos-v5
Source: Original Platform

.gitattributes

@@ -0,0 +1,28 @@
+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin 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
+*.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
+model.safetensors filter=lfs diff=lfs merge=lfs -text

1_Pooling/config.json

@@ -0,0 +1,7 @@
+{
+  "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

@@ -0,0 +1,140 @@
+---
+language:
+- en
+library_name: sentence-transformers
+tags:
+- sentence-transformers
+- feature-extraction
+- sentence-similarity
+- transformers
+pipeline_tag: sentence-similarity
+---
+
+# msmarco-MiniLM-L12-cos-v5
+This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and was designed for **semantic search**. It has been trained on 500k (query, answer) pairs from the [MS MARCO Passages dataset](https://github.com/microsoft/MSMARCO-Passage-Ranking). For an introduction to semantic search, have a look at: [SBERT.net - Semantic Search](https://www.sbert.net/examples/applications/semantic-search/README.html)
+
+
+## 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, util
+
+query = "How many people live in London?"
+docs = ["Around 9 Million people live in London", "London is known for its financial district"]
+
+#Load the model
+model = SentenceTransformer('sentence-transformers/msmarco-MiniLM-L12-cos-v5')
+
+#Encode query and documents
+query_emb = model.encode(query)
+doc_emb = model.encode(docs)
+
+#Compute dot score between query and all document embeddings
+scores = util.dot_score(query_emb, doc_emb)[0].cpu().tolist()
+
+#Combine docs & scores
+doc_score_pairs = list(zip(docs, scores))
+
+#Sort by decreasing score
+doc_score_pairs = sorted(doc_score_pairs, key=lambda x: x[1], reverse=True)
+
+#Output passages & scores
+for doc, score in doc_score_pairs:
+    print(score, doc)
+```
+
+
+## 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 correct 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 average of all tokens
+def mean_pooling(model_output, attention_mask):
+    token_embeddings = model_output.last_hidden_state #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)
+
+
+#Encode text
+def encode(texts):
+    # Tokenize sentences
+    encoded_input = tokenizer(texts, padding=True, truncation=True, return_tensors='pt')
+
+    # Compute token embeddings
+    with torch.no_grad():
+        model_output = model(**encoded_input, return_dict=True)
+
+    # Perform pooling
+    embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
+
+    # Normalize embeddings
+    embeddings = F.normalize(embeddings, p=2, dim=1)
+	
+    return embeddings
+
+
+# Sentences we want sentence embeddings for
+query = "How many people live in London?"
+docs = ["Around 9 Million people live in London", "London is known for its financial district"]
+
+# Load model from HuggingFace Hub
+tokenizer = AutoTokenizer.from_pretrained("sentence-transformers/msmarco-MiniLM-L12-cos-v5")
+model = AutoModel.from_pretrained("sentence-transformers/msmarco-MiniLM-L12-cos-v5")
+
+#Encode query and docs
+query_emb = encode(query)
+doc_emb = encode(docs)
+
+#Compute dot score between query and all document embeddings
+scores = torch.mm(query_emb, doc_emb.transpose(0, 1))[0].cpu().tolist()
+
+#Combine docs & scores
+doc_score_pairs = list(zip(docs, scores))
+
+#Sort by decreasing score
+doc_score_pairs = sorted(doc_score_pairs, key=lambda x: x[1], reverse=True)
+
+#Output passages & scores
+for doc, score in doc_score_pairs:
+    print(score, doc)
+```
+
+## Technical Details
+
+In the following some technical details how this model must be used:
+
+| Setting | Value |
+| --- | :---: |
+| Dimensions | 768 |
+| Produces normalized embeddings | Yes |
+| Pooling-Method | Mean pooling |
+| Suitable score functions | dot-product (`util.dot_score`), cosine-similarity (`util.cos_sim`), or euclidean distance |
+
+Note: When loaded with `sentence-transformers`, this model produces normalized embeddings with length 1. In that case, dot-product and cosine-similarity are equivalent. dot-product is preferred as it is faster. Euclidean distance is proportional to dot-product and can also be used.
+
+## 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",
+}
+```

config.json

@@ -0,0 +1,24 @@
+{
+  "_name_or_path": "old_models/msmarco-MiniLM-L-12-v3/0_Transformer",
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "gradient_checkpointing": false,
+  "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",
+  "transformers_version": "4.7.0",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

config_sentence_transformers.json

@@ -0,0 +1,7 @@
+{
+  "__version__": {
+    "sentence_transformers": "2.0.0",
+    "transformers": "4.7.0",
+    "pytorch": "1.9.0+cu102"
+  }
+}

configuration.json

@@ -0,0 +1 @@
+{"framework": "pytorch", "task": "sentence-similarity", "allow_remote": true}

flax_model.msgpack

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:cc545410e357acc8d666a3dd2ab33a9b3cd922eb4a9b94436f0790b399e8b3d4
+size 133447149

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:de1a3145d5bdeebb9d7a8294f05603adfb395cbf67c174697c6e42918f8ae17f
+size 133466304

modules.json

@@ -0,0 +1,20 @@
+[
+  {
+    "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/model.onnx

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:619fcdd0026b6e2ed2d5c54d93dbe0efe84a65662474adde564b08aa92820eeb
+size 133126567

onnx/model_O1.onnx

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:49c165363f5f3918d46ff61dbc442d1077eaaddeb3e9a81c9201639510420649
+size 133037320

onnx/model_O2.onnx

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b9f0832be9f373b715e3fdb6342a65946afc14192ef24a240fb22cb8f944f719
+size 132970874

onnx/model_O3.onnx

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:930322e845a6107a2ba1b564523d2c72fa6e69169d1a64ea2aa64a4d1685f864
+size 132970729

onnx/model_O4.onnx

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:34e29ab54d673289fbb9178474e60f47fb60c497a3c08e2e7e1a95cd509fcbca
+size 66578744

onnx/model_qint8_arm64.onnx

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:782aecc94bd11a9d17a5dc7136970c9fa5397827f789937075feada2a3458d21
+size 34118638

onnx/model_qint8_avx512.onnx

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:782aecc94bd11a9d17a5dc7136970c9fa5397827f789937075feada2a3458d21
+size 34118638

onnx/model_qint8_avx512_vnni.onnx

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:782aecc94bd11a9d17a5dc7136970c9fa5397827f789937075feada2a3458d21
+size 34118638

onnx/model_quint8_avx2.onnx

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:550222139953f6eb9de5046e07396244a27393e50f25d4319fc505cbcf15a22f
+size 34160110

openvino/openvino_model.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:98681a746c2465cfa12e483c7545ffe16a7768be493bfb75291b25beb7f63913
+size 132852880

openvino/openvino_model_qint8_quantized.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:cb59513b412cef1acd8d55609f398ff9b51ed89051f630b733f32b68af2bff8c
+size 33818048

pytorch_model.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b17442e1b4a5db687084be01f47d35f683f35ffadbfe26ca42622fec51d013a8
+size 133518577

sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+  "max_seq_length": 384,
+  "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]"}

tf_model.h5

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:babd8eaec24a64d327fa18a0e455567d953b766d4dfcc68daa699b07304eba29
+size 133724136

tokenizer_config.json

@@ -0,0 +1 @@
+{"do_lower_case": true, "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, "name_or_path": "old_models/msmarco-MiniLM-L-12-v3/0_Transformer", "do_basic_tokenize": true, "never_split": null, "special_tokens_map_file": "old_models/msmarco-MiniLM-L-12-v3/0_Transformer/special_tokens_map.json"}