Update README.md

README.md

@@ -4,12 +4,18 @@ tags:
 - sentence-transformers
 - feature-extraction
 - sentence-similarity
-
+- transformers
+license: mit
+datasets:
+- jaimevera1107/similarity-sentences-spanish
+language:
+- es
+library_name: sentence-transformers
 ---
 
-# {MODEL_NAME}
+# All-MiniLM-L6-v2 Fine Tuned - Sentence Transformers - Embedding Model (Spanish-Español)
 
-This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
+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.
 
 <!--- Describe your model here -->
 
@@ -25,20 +31,58 @@ Then you can use the model like this:
 
 ```python
 from sentence_transformers import SentenceTransformer
-sentences = ["This is an example sentence", "Each sentence is converted"]
+sentences = ["Esta es una frase para ser comparada", "Esta es otra oración"]
 
-model = SentenceTransformer('{MODEL_NAME}')
+model = SentenceTransformer('jaimevera1107/roberta-similarity-es')
 embeddings = model.encode(sentences)
 print(embeddings)
 ```
 
 
 
-## Evaluation Results
+## 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)
 
-<!--- Describe how your model was evaluated -->
 
-For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
+# Sentences we want sentence embeddings for
+sentences = ["Esta es una frase para ser comparada", "Esta es otra oración"]
+
+# Load model from HuggingFace Hub
+tokenizer = AutoTokenizer.from_pretrained('jaimevera1107/roberta-similarity-es')
+model = AutoModel.from_pretrained('jaimevera1107/roberta-similarity-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)
+```
+
+
+
+## Evaluation Results
+
+| Model                   | R squared  | Spearman Correlation |
+|----------------------------|--------------|-------------------------|
+| Roberta Fine tuned         | 70.67 % | 83.41 % |
 
 
 ## Training
@@ -51,6 +95,8 @@ The model was trained with the parameters:
 {'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
 ```
 
+The data used was the one in the [Similarity Sentences Spanish Dataset](https://huggingface.co/datasets/jaimevera1107/similarity-sentences-spanish)
+
 **Loss**:
 
 `sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss` 
@@ -77,12 +123,7 @@ Parameters of the fit()-Method:
 ## Full Model Architecture
 ```
 SentenceTransformer(
-  (0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel 
-  (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
-  (2): Normalize()
+  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: RobertaModel 
+  (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 -->
+```