Update README.md

README.md

@@ -18,9 +18,11 @@ datasets:
 - indonesian-nlp/lfqa_id
 - jakartaresearch/indoqa
 - jakartaresearch/id-paraphrase-detection
+language:
+- ind
 ---
 
-# LazarusNLP/all-multilingual-e5-small
+# LazarusNLP/all-indo-e5-small-v2
 
 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.
 
@@ -40,7 +42,7 @@ 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('LazarusNLP/all-multilingual-e5-small')
+model = SentenceTransformer('LazarusNLP/all-indo-e5-small-v2')
 embeddings = model.encode(sentences)
 print(embeddings)
 ```
@@ -66,8 +68,8 @@ 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('LazarusNLP/all-multilingual-e5-small')
-model = AutoModel.from_pretrained('LazarusNLP/all-multilingual-e5-small')
+tokenizer = AutoTokenizer.from_pretrained('LazarusNLP/all-indo-e5-small-v2')
+model = AutoModel.from_pretrained('LazarusNLP/all-indo-e5-small-v2')
 
 # Tokenize sentences
 encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
@@ -89,7 +91,7 @@ print(sentence_embeddings)
 
 <!--- 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=LazarusNLP/all-multilingual-e5-small)
+For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=LazarusNLP/all-indo-e5-small-v2)
 
 
 ## Training
@@ -99,7 +101,7 @@ The model was trained with the parameters:
 
 `MultiDatasetDataLoader.MultiDatasetDataLoader` of length 968 with parameters:
 ```
-{'batch_size': 'unknown'}
+{'batch_size_pairs': 384, 'batch_size_triplets': 256}
 ```
 
 **Loss**: