Datasets:

akhooli
/

arabic-triplets-1m-curated-sims-len

@@ -32,7 +32,7 @@ configs:
 ---
-# Arabic 1 Million Triplets (curated):
 This is a curated dataset to use in Arabic ColBERT and SBERT models (among other uses).
 In addition to `anchor`, `positive` and `negative` columns, the dataset has two columns: `sim_pos` and `sim_neg` which are cosine
 similarities between the anchor (query) and bothe positive and negative examples.
@@ -42,12 +42,13 @@ The cosine similarity uses an embedding model by [AbderrahmanSkiredj1/Arabic_tex
 (inspired by [Omar Nicar](https://huggingface.co/Omartificial-Intelligence-Space)) who made the
 [first Arabic SBERT embeddings model](https://huggingface.co/Omartificial-Intelligence-Space/Arabert-all-nli-triplet-Matryoshka) and a triplets dataset based on NLI.
-# Why another dataset?
 While training an Arabic ColBERT model using a sample from the mMARCO dataset, I noticed retrieval issues. It is true all these triplet datasets are translated, but
 quality was not up to expectation. I took the dataset used by the embedding model (which is NLI plus some 300K) and 1 million samples from mMARCO and removed
 lines that had seperate latin words/phrases and sampled 1 million rows of the combined data. Then I added the similiarity columns and lengths.
 This should enable researchers and users to filter based on several criteria (including hard negatives). This is not saying the model used in similarities was perfect.
 In some cases, exmples annotated as negative were identical to the anchor/query. Adding the similarities columns took more time than training models.
-# Arabic SBERT and ColBERT models:
-Filtered subsets based on certain criteria show impressive perfrmance. Models will be uploaded and linked from here when ready.

 ---
+# Arabic 1 Million Triplets (curated):
 This is a curated dataset to use in Arabic ColBERT and SBERT models (among other uses).
 In addition to `anchor`, `positive` and `negative` columns, the dataset has two columns: `sim_pos` and `sim_neg` which are cosine
 similarities between the anchor (query) and bothe positive and negative examples.
 (inspired by [Omar Nicar](https://huggingface.co/Omartificial-Intelligence-Space)) who made the
 [first Arabic SBERT embeddings model](https://huggingface.co/Omartificial-Intelligence-Space/Arabert-all-nli-triplet-Matryoshka) and a triplets dataset based on NLI.
+# Why another dataset?
 While training an Arabic ColBERT model using a sample from the mMARCO dataset, I noticed retrieval issues. It is true all these triplet datasets are translated, but
 quality was not up to expectation. I took the dataset used by the embedding model (which is NLI plus some 300K) and 1 million samples from mMARCO and removed
 lines that had seperate latin words/phrases and sampled 1 million rows of the combined data. Then I added the similiarity columns and lengths.
 This should enable researchers and users to filter based on several criteria (including hard negatives). This is not saying the model used in similarities was perfect.
 In some cases, exmples annotated as negative were identical to the anchor/query. Adding the similarities columns took more time than training models.
+# Arabic SBERT and ColBERT models:
+Filtered subsets based on certain criteria show impressive perfrmance. Models will be uploaded and linked from here when ready.
+If you saw earlier versions of triplets datasets under this account, they have been removed in favor of this one.