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Model: mrm8488/distiluse-base-multilingual-cased-v2-finetuned-stsb_multi_mt-es
Source: Original Platform

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1_Pooling/config.json

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+{
+  "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
+}

README.md

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+---
+language: es
+thumbnail: https://imgur.com/a/G77ZqQN
+pipeline_tag: sentence-similarity
+datasets:
+- stsb_multi_mt
+tags:
+- sentence-transformers
+- feature-extraction
+- sentence-similarity
+- transformers
+---
+
+# Distiluse-m-v2 fine-tuned on stsb_multi_mt for Spanish Semantic Textual Similarity
+
+This is a [sentence-transformers](https://www.SBERT.net) model (distiluse-base-multilingual-cased-v2): It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
+
+<!--- Describe your model here -->
+
+## 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 = ["Nerea va a comprar un cuadro usando bitcoins", "Se puede comprar arte con bitcoins"]
+
+model = SentenceTransformer('mrm8488/distiluse-base-multilingual-cased-v2-finetuned-stsb_multi_mt-es')
+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 = ["Nerea va a comprar un cuadro usando bitcoins", "Se puede comprar arte con bitcoins"]
+
+# Load model from HuggingFace Hub
+tokenizer = AutoTokenizer.from_pretrained('mrm8488/distiluse-base-multilingual-cased-v2-finetuned-stsb_multi_mt-es')
+model = AutoModel.from_pretrained('mrm8488/distiluse-base-multilingual-cased-v2-finetuned-stsb_multi_mt-es')
+
+# 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, mean pooling.
+sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
+
+print("Sentence embeddings:")
+print(sentence_embeddings)
+```
+
+## How to evaluate
+
+```py
+from datasets import load_dataset
+from sentence_transformers import SentenceTransformer, InputExample
+from sentence_transformers.evaluation import EmbeddingSimilarityEvaluator
+
+ 
+test_data = load_dataset('stsb_multi_mt', 'es', split='test')
+test_data = test_data.rename_columns({'similarity_score': 'label'})
+test_data = test_data.map(lambda x: {'label': x['label'] / 5.0})
+
+samples = []
+for sample in test_data:
+    samples.append(InputExample(
+        texts=[sample['sentence1'], sample['sentence2']],
+        label=sample['label']
+    ))
+    
+evaluator = EmbeddingSimilarityEvaluator.from_input_examples(
+    samples, write_csv=False
+)
+
+model = SentenceTransformer('mrm8488/distiluse-base-multilingual-cased-v2-finetuned-stsb_multi_mt-es')
+
+evaluator(model)
+
+# It outputs: 0.7604056195656299
+```
+
+## Evaluation Results
+
+ **Spearman’s rank correlation: 0.7604056195656299**
+
+For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
+
+
+## Training
+The model was trained with the parameters:
+
+**DataLoader**:
+
+`sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader` of length 906 with parameters:
+```
+{'batch_size': 16}
+```
+
+**Loss**:
+
+`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
+  ```
+  {'scale': 20.0, 'similarity_fct': 'cos_sim'}
+  ```
+
+Parameters of the fit()-Method:
+```
+{
+    "epochs": 3,
+    "evaluation_steps": 0,
+    "evaluator": "NoneType",
+    "max_grad_norm": 1,
+    "optimizer_class": "<class 'transformers.optimization.AdamW'>",
+    "optimizer_params": {
+        "lr": 2e-05
+    },
+    "scheduler": "WarmupLinear",
+    "steps_per_epoch": null,
+    "warmup_steps": 271,
+    "weight_decay": 0.01
+}
+```
+
+
+## Full Model Architecture
+```
+SentenceTransformer(
+  (0): Transformer({'max_seq_length': 512, '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})
+)
+```
+
+## Citing & Authors
+
+<!--- Describe where people can find more information -->

config.json

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+{
+  "_name_or_path": "sentence-transformers/distiluse-base-multilingual-cased-v2",
+  "activation": "gelu",
+  "architectures": [
+    "DistilBertModel"
+  ],
+  "attention_dropout": 0.1,
+  "dim": 768,
+  "dropout": 0.1,
+  "hidden_dim": 3072,
+  "initializer_range": 0.02,
+  "max_position_embeddings": 512,
+  "model_type": "distilbert",
+  "n_heads": 12,
+  "n_layers": 6,
+  "output_hidden_states": true,
+  "output_past": true,
+  "pad_token_id": 0,
+  "qa_dropout": 0.1,
+  "seq_classif_dropout": 0.2,
+  "sinusoidal_pos_embds": false,
+  "tie_weights_": true,
+  "torch_dtype": "float32",
+  "transformers_version": "4.16.2",
+  "vocab_size": 119547
+}

config_sentence_transformers.json

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+{
+  "__version__": {
+    "sentence_transformers": "2.1.0",
+    "transformers": "4.16.2",
+    "pytorch": "1.10.0+cu111"
+  }
+}

modules.json

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

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sentence_bert_config.json

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+{
+  "max_seq_length": 512,
+  "do_lower_case": false
+}

special_tokens_map.json

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+{"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"}

tokenizer_config.json

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+{"do_lower_case": false, "unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]", "tokenize_chinese_chars": true, "strip_accents": null, "max_len": 512, "special_tokens_map_file": "/home/reimers/.cache/torch/sentence_transformers/sbert.net_models_distiluse-base-multilingual-cased/0_DistilBERT/special_tokens_map.json", "full_tokenizer_file": null, "name_or_path": "sentence-transformers/distiluse-base-multilingual-cased-v2", "do_basic_tokenize": true, "never_split": null, "tokenizer_class": "DistilBertTokenizer"}