Update README.md

README.md

@@ -1,3 +1,75 @@
 ---
+pipeline_tag: sentence-similarity
+language: multilingual
 license: apache-2.0
+tags:
+- sentence-transformers
+- feature-extraction
+- sentence-similarity
+- transformers
 ---
+
+# ONNX convert distiluse-base-multilingual-cased-v2
+## Conversion of [sentence-transformers/distiluse-base-multilingual-cased-v2](https://huggingface.co/sentence-transformers/distiluse-base-multilingual-cased-v2)
+This is a [sentence-transformers](https://www.SBERT.net) ONNX model: It maps sentences & paragraphs to a 512 dimensional dense vector space and can be used for tasks like clustering or semantic search. This custom model outputs `last_hidden_state` similar like original sentence-transformer implementation.
+
+
+
+## Usage (HuggingFace Optimum)
+Using this model becomes easy when you have [optimum](https://github.com/huggingface/optimum) installed:
+```
+python -m pip install optimum
+```
+Then you can use the model like this:
+```python
+from optimum.onnxruntime.modeling_ort import ORTModelForCustomTasks
+
+model = ORTModelForCustomTasks.from_pretrained("lorenpe2/distiluse-base-multilingual-cased-v2")
+tokenizer = AutoTokenizer.from_pretrained("lorenpe2/distiluse-base-multilingual-cased-v2")
+inputs = tokenizer("I love burritos!", return_tensors="pt")
+pred = model(**inputs)
+```
+You will also be able to leverage the pipeline API in transformers:
+```python
+from transformers import pipeline
+
+onnx_extractor = pipeline("feature-extraction", model=model, tokenizer=tokenizer)
+text = "I love burritos!"
+pred = onnx_extractor(text)
+```
+
+
+
+## Evaluation Results
+
+
+
+For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=sentence-transformers/distiluse-base-multilingual-cased-v2)
+
+
+
+## Full Model Architecture
+```
+SentenceTransformer(
+  (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: DistilBertModel 
+  (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})
+  (2): Dense({'in_features': 768, 'out_features': 512, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'})
+)
+```
+
+## 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",
+}
+```