Update README.md

README.md

@@ -7,7 +7,7 @@ tags:
 - transformers
 ---
 
-# {MODEL_NAME}
+# hiiamsid/sentence_similarity_spanish_es
 
 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.
 
@@ -27,7 +27,7 @@ Then you can use the model like this:
 from sentence_transformers import SentenceTransformer
 sentences = ["This is an example sentence", "Each sentence is converted"]
 
-model = SentenceTransformer('{MODEL_NAME}')
+model = SentenceTransformer('hiiamsid/sentence_similarity_spanish_es')
 embeddings = model.encode(sentences)
 print(embeddings)
 ```
@@ -53,8 +53,8 @@ def mean_pooling(model_output, attention_mask):
 sentences = ['This is an example sentence', 'Each sentence is converted']
 
 # Load model from HuggingFace Hub
-tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
-model = AutoModel.from_pretrained('{MODEL_NAME}')
+tokenizer = AutoTokenizer.from_pretrained('hiiamsid/sentence_similarity_spanish_es')
+model = AutoModel.from_pretrained('hiiamsid/sentence_similarity_spanish_es')
 
 # Tokenize sentences
 encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
@@ -73,10 +73,15 @@ print(sentence_embeddings)
 
 
 ## Evaluation Results
+```
+epoch,steps,cosine_pearson,cosine_spearman,euclidean_pearson,euclidean_spearman,manhattan_pearson,manhattan_spearman,dot_pearson,dot_spearman
+0,-1,0.8441511943881872,0.842139456614267,0.8079280123390863,0.8133773844408285,0.8061045432939875,0.8120313077224331,0.765564331583017,0.7758911212599943
+1,-1,0.8505781800765124,0.8483036121137681,0.824043571251859,0.828168962987315,0.8227441143847719,0.8269561222926696,0.7895885844553006,0.7956808786541772
+2,-1,0.8540176327819012,0.8524414878771152,0.8278950073844416,0.8323727470271813,0.8268352374519953,0.8316947483386466,0.8027944039984911,0.8062262061975706
+3,-1,0.8532432895679434,0.851780980304441,0.8298723338734442,0.8340725567708174,0.8289668426348045,0.8333822263715207,0.8043286320186501,0.8063301333153703
+```
 
-<!--- 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})
+For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=hiiamsid/sentence_similarity_spanish_es)
 
 
 ## Training