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--- |
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license: mit |
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dataset_info: |
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features: |
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- name: anchor |
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dtype: string |
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- name: positive |
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dtype: string |
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- name: negative |
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dtype: string |
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- name: sim_pos |
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dtype: float64 |
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- name: sim_neg |
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dtype: float64 |
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- name: len_anc |
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dtype: int64 |
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- name: len_pos |
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dtype: int64 |
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- name: len_neg |
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dtype: int64 |
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splits: |
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- name: train |
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num_bytes: 614206347 |
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num_examples: 1000000 |
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download_size: 308842392 |
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dataset_size: 614206347 |
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configs: |
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- config_name: default |
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data_files: |
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- split: train |
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path: data/train-* |
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--- |
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# Arabic 1 Million Triplets (curated): |
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This is a curated dataset to use in Arabic ColBERT and SBERT models (among other uses). |
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In addition to `anchor`, `positive` and `negative` columns, the dataset has two columns: `sim_pos` and `sim_neg` which are cosine |
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similarities between the anchor (query) and bothe positive and negative examples. |
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The last 3 columns are lengths (words) for each of the `anchor`, `positive` and `negative` examples. Length uses simple split on space, not tokens. |
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The cosine similarity uses an embedding model by [AbderrahmanSkiredj1/Arabic_text_embedding_for_sts](https://huggingface.co/AbderrahmanSkiredj1/Arabic_text_embedding_for_sts) |
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(inspired by [Omar Nicar](https://huggingface.co/Omartificial-Intelligence-Space)) who made the |
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[first Arabic SBERT embeddings model](https://huggingface.co/Omartificial-Intelligence-Space/Arabert-all-nli-triplet-Matryoshka) and a triplets dataset based on NLI. |
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# Why another dataset? |
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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 |
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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 |
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lines that had seperate latin words/phrases and sampled 1 million rows of the combined data. Then I added the similiarity columns and lengths. |
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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. |
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In some cases, exmples annotated as negative were identical to the anchor/query. Adding the similarities columns took more time than training models. |
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# Arabic SBERT and ColBERT models: |
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Filtered subsets based on certain criteria show impressive perfrmance. Models will be uploaded and linked from here when ready. |
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If you saw earlier versions of triplets datasets under this account, they have been removed in favor of this one. If you downloaded or duplicated a triplets |
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dataset from this account prior to Satuday 3 PM Jerusalem time on July 27th, 2024, you are also advised to get the updated version. |
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