初始化项目，由ModelHub XC社区提供模型

Model: Sahajtomar/french_semantic
Source: Original Platform

.gitattributes

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+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text

0_Transformer/config.json

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+{
+  "_name_or_path": "camembert/camembert-large",
+  "architectures": [
+    "CamembertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "eos_token_id": 2,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 1024,
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "camembert",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 24,
+  "output_past": true,
+  "pad_token_id": 1,
+  "position_embedding_type": "absolute",
+  "transformers_version": "4.5.0",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 32005
+}

0_Transformer/pytorch_model.bin

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0_Transformer/sentence_bert_config.json

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+  "do_lower_case": false
+}

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0_Transformer/special_tokens_map.json

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0_Transformer/tokenizer_config.json

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1_Pooling/config.json

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+{
+  "word_embedding_dimension": 1024,
+  "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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+---
+language: fr
+tags:
+- semantic
+- sentence-transformers
+- sentence-similarity
+- fr
+datasets:
+- sts
+---
+# French STS
+## STS dev (french)
+87.4%
+## STS test (french)
+85.8%
+#### STS pipeline
+```python
+!pip install -U sentence-transformers
+from sentence_transformers import SentenceTransformer
+model = SentenceTransformer('..model_path..')
+sentences1 = ["J'aime mon téléphone",
+    "Mon téléphone n'est pas bon.",
+    "Votre téléphone portable est superbe."]
+    
+sentences2 = ["Est-ce qu'il neige demain?",
+    "Récemment, de nombreux ouragans ont frappé les États-Unis",
+    "Le réchauffement climatique est réel",]
+embeddings1 = model.encode(sentences1, convert_to_tensor=True)
+embeddings2 = model.encode(sentences2, convert_to_tensor=True)
+cosine_scores = util.pytorch_cos_sim(embeddings1, embeddings2)
+for i in range(len(sentences1)):
+    for j in range(len(sentences2)):
+        print(cosine_scores[i][j]))
+"""
+""" 
+```

config.json

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+{
+  "__version__": "1.0.4"
+}

modules.json

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

similarity_evaluation_sts-dev_results.csv

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+3,-1,0.8744578309597704,0.8730785971619869,0.8624775706547827,0.8644809663305459,0.8623524115539193,0.864286517918685,0.8514432955875211,0.8483253736946224

similarity_evaluation_sts-test_results.csv

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+epoch,steps,cosine_pearson,cosine_spearman,euclidean_pearson,euclidean_spearman,manhattan_pearson,manhattan_spearman,dot_pearson,dot_spearman
+-1,-1,0.85801451596893,0.8554828594121078,0.848554409521124,0.853157512282925,0.8485465810034418,0.8531020401425077,0.8291477964682629,0.8225415231180051