初始化项目，由ModelHub XC社区提供模型

Model: sentence-transformers/msmarco-roberta-base-ance-firstp
Source: Original Platform

README.md

@@ -0,0 +1,52 @@
+---
+license: apache-2.0
+library_name: sentence-transformers
+tags:
+- sentence-transformers
+- feature-extraction
+- sentence-similarity
+pipeline_tag: sentence-similarity
+---
+
+# sentence-transformers/msmarco-roberta-base-ance-firstp
+
+This is a port of the [ANCE FirstP Model](https://github.com/microsoft/ANCE/) to [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.
+
+
+
+## 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('sentence-transformers/msmarco-roberta-base-ance-firstp')
+embeddings = model.encode(sentences)
+print(embeddings)
+```
+
+
+
+## Full Model Architecture
+```
+SentenceTransformer(
+  (0): Transformer({'max_seq_length': 512, 'do_lower_case': True}) with Transformer model: RobertaModel 
+  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
+  (2): Dense({'in_features': 768, 'out_features': 768, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
+  (3): LayerNorm(
+    (norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
+  )
+)
+```
+
+## Citing & Authors
+
+Have a look at: [ANCE Model](https://github.com/microsoft/ANCE/)