Update README.md

README.md

@@ -5,7 +5,12 @@ tags:
 - feature-extraction
 - sentence-similarity
 - transformers
-
+language:
+- pl
+license: lgpl-3.0
+library_name: sentence-transformers
+datasets:
+- radlab/polish-sts-dataset
 ---
 
 # polish-roberta-large-v2-sts
@@ -26,15 +31,14 @@ 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 = ["Ala ma kota", "Ala ma psa"]
 
-model = SentenceTransformer('{MODEL_NAME}')
+model = SentenceTransformer('radlab/polish-roberta-large-v2-sts')
 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.
 
@@ -51,11 +55,11 @@ def mean_pooling(model_output, attention_mask):
 
 
 # Sentences we want sentence embeddings for
-sentences = ['This is an example sentence', 'Each sentence is converted']
+sentences = ['Ala ma kota', 'Ala ma psa']
 
 # Load model from HuggingFace Hub
-tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
-model = AutoModel.from_pretrained('{MODEL_NAME}')
+tokenizer = AutoTokenizer.from_pretrained('radlab/polish-roberta-large-v2-sts')
+model = AutoModel.from_pretrained('radlab/polish-roberta-large-v2-sts')
 
 # Tokenize sentences
 encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
@@ -72,14 +76,6 @@ print(sentence_embeddings)
 ```
 
 
-
-## Evaluation Results
-
-<!--- 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})
-
-
 ## Training
 The model was trained with the parameters:
 
@@ -120,7 +116,3 @@ SentenceTransformer(
  (1): Pooling({'word_embedding_dimension': 1024, '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 -->