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Model: sentence-transformers/xlm-r-bert-base-nli-mean-tokens
Source: Original Platform
---
 .gitattributes                              |    19 +
 1_Pooling/config.json                       |     7 +
 README.md                                   |   103 +
 config.json                                 |    27 +
 config_sentence_transformers.json           |     7 +
 model.safetensors                           |     3 +
 modules.json                                |    14 +
 onnx/model.onnx                             |     3 +
 onnx/model_O1.onnx                          |     3 +
 onnx/model_O2.onnx                          |     3 +
 onnx/model_O3.onnx                          |     3 +
 onnx/model_O4.onnx                          |     3 +
 onnx/model_qint8_arm64.onnx                 |     3 +
 onnx/model_qint8_avx512.onnx                |     3 +
 onnx/model_qint8_avx512_vnni.onnx           |     3 +
 onnx/model_quint8_avx2.onnx                 |     3 +
 openvino/openvino_model.bin                 |     3 +
 openvino/openvino_model.xml                 | 13353 +++++++++++
 openvino/openvino_model_qint8_quantized.bin |     3 +
 openvino/openvino_model_qint8_quantized.xml | 22049 ++++++++++++++++++
 pytorch_model.bin                           |     3 +
 sentence_bert_config.json                   |     4 +
 sentencepiece.bpe.model                     |     3 +
 special_tokens_map.json                     |     1 +
 tf_model.h5                                 |     3 +
 tokenizer.json                              |     3 +
 tokenizer_config.json                       |     1 +
 27 files changed, 35633 insertions(+)
 create mode 100644 .gitattributes
 create mode 100644 1_Pooling/config.json
 create mode 100644 README.md
 create mode 100644 config.json
 create mode 100644 config_sentence_transformers.json
 create mode 100644 model.safetensors
 create mode 100644 modules.json
 create mode 100644 onnx/model.onnx
 create mode 100644 onnx/model_O1.onnx
 create mode 100644 onnx/model_O2.onnx
 create mode 100644 onnx/model_O3.onnx
 create mode 100644 onnx/model_O4.onnx
 create mode 100644 onnx/model_qint8_arm64.onnx
 create mode 100644 onnx/model_qint8_avx512.onnx
 create mode 100644 onnx/model_qint8_avx512_vnni.onnx
 create mode 100644 onnx/model_quint8_avx2.onnx
 create mode 100644 openvino/openvino_model.bin
 create mode 100644 openvino/openvino_model.xml
 create mode 100644 openvino/openvino_model_qint8_quantized.bin
 create mode 100644 openvino/openvino_model_qint8_quantized.xml
 create mode 100644 pytorch_model.bin
 create mode 100644 sentence_bert_config.json
 create mode 100644 sentencepiece.bpe.model
 create mode 100644 special_tokens_map.json
 create mode 100644 tf_model.h5
 create mode 100644 tokenizer.json
 create mode 100644 tokenizer_config.json

diff --git a/.gitattributes b/.gitattributes
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index 0000000..707fc07
--- /dev/null
+++ b/.gitattributes
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+*.bin.* filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tar.gz filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack 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
+pytorch_model.bin filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text
+model.safetensors filter=lfs diff=lfs merge=lfs -text
diff --git a/1_Pooling/config.json b/1_Pooling/config.json
new file mode 100644
index 0000000..4e09f29
--- /dev/null
+++ b/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
+}
\ No newline at end of file
diff --git a/README.md b/README.md
new file mode 100644
index 0000000..9e6275f
--- /dev/null
+++ b/README.md
@@ -0,0 +1,103 @@
+---
+license: apache-2.0
+library_name: sentence-transformers
+tags:
+- sentence-transformers
+- feature-extraction
+- sentence-similarity
+- transformers
+pipeline_tag: sentence-similarity
+---
+
+**⚠️ This model is deprecated. Please don't use it as it produces sentence embeddings of low quality. You can find recommended sentence embedding models here: [SBERT.net - Pretrained Models](https://www.sbert.net/docs/pretrained_models.html)**
+
+
+# sentence-transformers/xlm-r-bert-base-nli-mean-tokens
+
+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.
+
+
+
+## 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/xlm-r-bert-base-nli-mean-tokens')
+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('sentence-transformers/xlm-r-bert-base-nli-mean-tokens')
+model = AutoModel.from_pretrained('sentence-transformers/xlm-r-bert-base-nli-mean-tokens')
+
+# 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, max pooling.
+sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
+
+print("Sentence embeddings:")
+print(sentence_embeddings)
+```
+
+
+
+## Full Model Architecture
+```
+SentenceTransformer(
+  (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: XLMRobertaModel 
+  (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
+
+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",
+}
+```
\ No newline at end of file
diff --git a/config.json b/config.json
new file mode 100644
index 0000000..a842b92
--- /dev/null
+++ b/config.json
@@ -0,0 +1,27 @@
+{
+  "_name_or_path": "old_models/xlm-r-bert-base-nli-mean-tokens/0_Transformer",
+  "architectures": [
+    "XLMRobertaModel"
+  ],
+  "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": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "xlm-roberta",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "output_past": true,
+  "pad_token_id": 1,
+  "position_embedding_type": "absolute",
+  "transformers_version": "4.7.0",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 250002
+}
diff --git a/config_sentence_transformers.json b/config_sentence_transformers.json
new file mode 100644
index 0000000..b974b34
--- /dev/null
+++ b/config_sentence_transformers.json
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+{
+  "__version__": {
+    "sentence_transformers": "2.0.0",
+    "transformers": "4.7.0",
+    "pytorch": "1.9.0+cu102"
+  }
+}
\ No newline at end of file
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+    "type": "sentence_transformers.models.Transformer"
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+  }
+]
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+<net name="Model18" version="11">
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+		<layer id="2" name="self.embeddings.word_embeddings.weight" type="Const" version="opset1">
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+			<output>
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+		</layer>
+		<layer id="3" name="__module.embeddings.word_embeddings/aten::embedding/Convert" type="Convert" version="opset1">
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