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--- |
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language: |
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- en |
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- ar |
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- cs |
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- de |
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- es |
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- fr |
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- it |
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- ja |
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- ko |
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- nl |
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- pt |
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- zh |
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license: apache-2.0 |
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library_name: transformers |
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tags: |
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- language |
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- granite |
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- embeddings |
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- multilingual |
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model-index: |
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- name: ibm-granite/granite-embedding-107m-multilingual |
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results: |
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- dataset: |
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type: miracl/mmteb-miracl |
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name: Miracl (en) |
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config: en |
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split: dev |
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task: |
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type: Retrieval |
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metrics: |
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- type: ndcg_at_1 |
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value: 0.41176 |
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- type: ndcg_at_10 |
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value: 0.46682 |
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- type: ndcg_at_100 |
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value: 0.54326 |
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- type: ndcg_at_1000 |
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value: 0.56567 |
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- type: ndcg_at_20 |
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value: 0.50157 |
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- type: ndcg_at_3 |
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value: 0.41197 |
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- type: ndcg_at_5 |
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value: 0.42086 |
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- type: recall_at_1 |
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value: 0.19322 |
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- type: recall_at_10 |
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value: 0.57721 |
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- type: recall_at_100 |
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value: 0.83256 |
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- type: recall_at_1000 |
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value: 0.95511 |
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- type: recall_at_20 |
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value: 0.6757 |
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- type: recall_at_3 |
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value: 0.37171 |
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- type: recall_at_5 |
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value: 0.44695 |
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- dataset: |
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type: miracl/mmteb-miracl |
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name: Miracl (ar) |
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config: ar |
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split: dev |
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task: |
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type: Retrieval |
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metrics: |
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- type: ndcg_at_1 |
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value: 0.55559 |
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- type: ndcg_at_10 |
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value: 0.62541 |
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- type: ndcg_at_100 |
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value: 0.67101 |
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- type: ndcg_at_1000 |
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value: 0.6805 |
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- type: ndcg_at_20 |
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value: 0.64739 |
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- type: ndcg_at_3 |
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value: 0.56439 |
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- type: ndcg_at_5 |
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value: 0.59347 |
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- type: recall_at_1 |
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value: 0.37009 |
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- type: recall_at_10 |
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value: 0.73317 |
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- type: recall_at_100 |
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value: 0.90066 |
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- type: recall_at_1000 |
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value: 0.96272 |
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- type: recall_at_20 |
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value: 0.80205 |
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- type: recall_at_3 |
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value: 0.56903 |
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- type: recall_at_5 |
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value: 0.6518 |
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- dataset: |
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type: miracl/mmteb-miracl |
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name: Miracl (bn) |
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config: bn |
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split: dev |
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task: |
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type: Retrieval |
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metrics: |
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- type: ndcg_at_1 |
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value: 0.56691 |
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- type: ndcg_at_10 |
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value: 0.65484 |
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- type: ndcg_at_100 |
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value: 0.70142 |
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- type: ndcg_at_1000 |
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value: 0.70994 |
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- type: ndcg_at_20 |
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value: 0.67838 |
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- type: ndcg_at_3 |
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value: 0.5988 |
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- type: ndcg_at_5 |
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value: 0.62718 |
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- type: recall_at_1 |
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value: 0.3605 |
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- type: recall_at_10 |
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value: 0.76854 |
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- type: recall_at_100 |
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value: 0.9285 |
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- type: recall_at_1000 |
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value: 0.97928 |
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- type: recall_at_20 |
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value: 0.83667 |
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- type: recall_at_3 |
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value: 0.61596 |
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- type: recall_at_5 |
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value: 0.69766 |
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- dataset: |
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type: miracl/mmteb-miracl |
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name: Miracl (de) |
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config: de |
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split: dev |
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task: |
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type: Retrieval |
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metrics: |
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- type: ndcg_at_1 |
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value: 0.41967 |
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- type: ndcg_at_10 |
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value: 0.45141 |
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- type: ndcg_at_100 |
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value: 0.53461 |
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- type: ndcg_at_1000 |
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value: 0.55463 |
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- type: ndcg_at_20 |
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value: 0.49012 |
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- type: ndcg_at_3 |
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value: 0.39486 |
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- type: ndcg_at_5 |
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value: 0.41496 |
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- type: recall_at_1 |
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value: 0.19494 |
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- type: recall_at_10 |
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value: 0.53774 |
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- type: recall_at_100 |
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value: 0.83314 |
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- type: recall_at_1000 |
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value: 0.95045 |
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- type: recall_at_20 |
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value: 0.65659 |
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- type: recall_at_3 |
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value: 0.3556 |
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- type: recall_at_5 |
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value: 0.44448 |
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- dataset: |
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type: miracl/mmteb-miracl |
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name: Miracl (es) |
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config: es |
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split: dev |
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task: |
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type: Retrieval |
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metrics: |
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- type: ndcg_at_1 |
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value: 0.54475 |
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- type: ndcg_at_10 |
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value: 0.46593 |
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- type: ndcg_at_100 |
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value: 0.58079 |
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- type: ndcg_at_1000 |
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value: 0.60656 |
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- type: ndcg_at_20 |
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value: 0.51858 |
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- type: ndcg_at_3 |
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value: 0.4578 |
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- type: ndcg_at_5 |
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value: 0.44321 |
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- type: recall_at_1 |
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value: 0.15966 |
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- type: recall_at_10 |
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value: 0.49343 |
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- type: recall_at_100 |
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value: 0.82684 |
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- type: recall_at_1000 |
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value: 0.95299 |
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- type: recall_at_20 |
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value: 0.62367 |
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- type: recall_at_3 |
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value: 0.2949 |
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- type: recall_at_5 |
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value: 0.37983 |
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- dataset: |
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type: miracl/mmteb-miracl |
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name: Miracl (fa) |
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config: fa |
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split: dev |
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task: |
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type: Retrieval |
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metrics: |
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- type: ndcg_at_1 |
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value: 0.36709 |
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- type: ndcg_at_10 |
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value: 0.46961 |
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- type: ndcg_at_100 |
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value: 0.53262 |
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- type: ndcg_at_1000 |
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value: 0.55024 |
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- type: ndcg_at_20 |
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value: 0.49892 |
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- type: ndcg_at_3 |
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value: 0.40235 |
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- type: ndcg_at_5 |
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value: 0.42866 |
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- type: recall_at_1 |
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value: 0.22735 |
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- type: recall_at_10 |
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value: 0.59949 |
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- type: recall_at_100 |
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value: 0.83867 |
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- type: recall_at_1000 |
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value: 0.95007 |
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- type: recall_at_20 |
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value: 0.68947 |
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- type: recall_at_3 |
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value: 0.41781 |
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- type: recall_at_5 |
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value: 0.49374 |
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- dataset: |
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type: miracl/mmteb-miracl |
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name: Miracl (fi) |
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config: fi |
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split: dev |
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task: |
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type: Retrieval |
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metrics: |
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- type: ndcg_at_1 |
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value: 0.59245 |
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- type: ndcg_at_10 |
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value: 0.65551 |
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- type: ndcg_at_100 |
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value: 0.6967 |
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- type: ndcg_at_1000 |
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value: 0.70521 |
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- type: ndcg_at_20 |
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value: 0.67552 |
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- type: ndcg_at_3 |
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value: 0.58876 |
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- type: ndcg_at_5 |
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value: 0.61779 |
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- type: recall_at_1 |
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value: 0.37669 |
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- type: recall_at_10 |
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value: 0.76529 |
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- type: recall_at_100 |
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value: 0.9156 |
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- type: recall_at_1000 |
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value: 0.96977 |
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- type: recall_at_20 |
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value: 0.82685 |
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- type: recall_at_3 |
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value: 0.60234 |
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- type: recall_at_5 |
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value: 0.67135 |
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- dataset: |
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type: miracl/mmteb-miracl |
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name: Miracl (fr) |
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config: fr |
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split: dev |
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task: |
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type: Retrieval |
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metrics: |
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- type: ndcg_at_1 |
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value: 0.38776 |
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- type: ndcg_at_10 |
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value: 0.47589 |
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- type: ndcg_at_100 |
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value: 0.54641 |
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- type: ndcg_at_1000 |
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value: 0.5629 |
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- type: ndcg_at_20 |
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value: 0.51203 |
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- type: ndcg_at_3 |
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value: 0.38924 |
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- type: ndcg_at_5 |
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value: 0.42572 |
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- type: recall_at_1 |
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value: 0.22082 |
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- type: recall_at_10 |
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value: 0.61619 |
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- type: recall_at_100 |
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value: 0.87237 |
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- type: recall_at_1000 |
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value: 0.97449 |
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- type: recall_at_20 |
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value: 0.72689 |
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- type: recall_at_3 |
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value: 0.39527 |
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- type: recall_at_5 |
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value: 0.48983 |
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- dataset: |
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type: miracl/mmteb-miracl |
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name: Miracl (hi) |
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config: hi |
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split: dev |
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task: |
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type: Retrieval |
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metrics: |
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- type: ndcg_at_1 |
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value: 0.33143 |
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- type: ndcg_at_10 |
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value: 0.42084 |
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- type: ndcg_at_100 |
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value: 0.48647 |
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- type: ndcg_at_1000 |
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value: 0.50712 |
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- type: ndcg_at_20 |
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value: 0.45399 |
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- type: ndcg_at_3 |
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value: 0.34988 |
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- type: ndcg_at_5 |
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value: 0.37938 |
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- type: recall_at_1 |
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value: 0.17852 |
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- type: recall_at_10 |
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value: 0.55217 |
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- type: recall_at_100 |
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value: 0.79929 |
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- type: recall_at_1000 |
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value: 0.93434 |
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- type: recall_at_20 |
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value: 0.65231 |
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- type: recall_at_3 |
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value: 0.33765 |
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- type: recall_at_5 |
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value: 0.43828 |
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- dataset: |
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type: miracl/mmteb-miracl |
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name: Miracl (id) |
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config: id |
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split: dev |
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task: |
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type: Retrieval |
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metrics: |
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- type: ndcg_at_1 |
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value: 0.43854 |
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- type: ndcg_at_10 |
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value: 0.45459 |
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- type: ndcg_at_100 |
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value: 0.53643 |
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- type: ndcg_at_1000 |
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value: 0.56052 |
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- type: ndcg_at_20 |
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value: 0.48795 |
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- type: ndcg_at_3 |
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value: 0.41041 |
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- type: ndcg_at_5 |
|
value: 0.42235 |
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- type: recall_at_1 |
|
value: 0.19193 |
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- type: recall_at_10 |
|
value: 0.5289 |
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- type: recall_at_100 |
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value: 0.79649 |
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- type: recall_at_1000 |
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value: 0.92937 |
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- type: recall_at_20 |
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value: 0.61813 |
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- type: recall_at_3 |
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value: 0.35431 |
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- type: recall_at_5 |
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value: 0.43348 |
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- dataset: |
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type: miracl/mmteb-miracl |
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name: Miracl (ja) |
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config: ja |
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split: dev |
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task: |
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type: Retrieval |
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metrics: |
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- type: ndcg_at_1 |
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value: 0.53256 |
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- type: ndcg_at_10 |
|
value: 0.59922 |
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- type: ndcg_at_100 |
|
value: 0.65407 |
|
- type: ndcg_at_1000 |
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value: 0.66484 |
|
- type: ndcg_at_20 |
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value: 0.62596 |
|
- type: ndcg_at_3 |
|
value: 0.53717 |
|
- type: ndcg_at_5 |
|
value: 0.56523 |
|
- type: recall_at_1 |
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value: 0.34555 |
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- type: recall_at_10 |
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value: 0.71476 |
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- type: recall_at_100 |
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value: 0.91152 |
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- type: recall_at_1000 |
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value: 0.97728 |
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- type: recall_at_20 |
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value: 0.79811 |
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- type: recall_at_3 |
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value: 0.53482 |
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- type: recall_at_5 |
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value: 0.62327 |
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- dataset: |
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type: miracl/mmteb-miracl |
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name: Miracl (ko) |
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config: ko |
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split: dev |
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task: |
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type: Retrieval |
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metrics: |
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- type: ndcg_at_1 |
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value: 0.5493 |
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- type: ndcg_at_10 |
|
value: 0.58413 |
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- type: ndcg_at_100 |
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value: 0.64374 |
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- type: ndcg_at_1000 |
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value: 0.65655 |
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- type: ndcg_at_20 |
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value: 0.61732 |
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- type: ndcg_at_3 |
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value: 0.53068 |
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- type: ndcg_at_5 |
|
value: 0.55202 |
|
- type: recall_at_1 |
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value: 0.32602 |
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- type: recall_at_10 |
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value: 0.68647 |
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- type: recall_at_100 |
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value: 0.87746 |
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- type: recall_at_1000 |
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value: 0.95524 |
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- type: recall_at_20 |
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value: 0.78089 |
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- type: recall_at_3 |
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value: 0.49173 |
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- type: recall_at_5 |
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value: 0.5827 |
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- dataset: |
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type: miracl/mmteb-miracl |
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name: Miracl (ru) |
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config: ru |
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split: dev |
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task: |
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type: Retrieval |
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metrics: |
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- type: ndcg_at_1 |
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value: 0.43131 |
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- type: ndcg_at_10 |
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value: 0.48262 |
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- type: ndcg_at_100 |
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value: 0.56158 |
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- type: ndcg_at_1000 |
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value: 0.57929 |
|
- type: ndcg_at_20 |
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value: 0.52023 |
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- type: ndcg_at_3 |
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value: 0.42808 |
|
- type: ndcg_at_5 |
|
value: 0.44373 |
|
- type: recall_at_1 |
|
value: 0.22018 |
|
- type: recall_at_10 |
|
value: 0.58034 |
|
- type: recall_at_100 |
|
value: 0.84074 |
|
- type: recall_at_1000 |
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value: 0.93938 |
|
- type: recall_at_20 |
|
value: 0.68603 |
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- type: recall_at_3 |
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value: 0.39307 |
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- type: recall_at_5 |
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value: 0.47077 |
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- dataset: |
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type: miracl/mmteb-miracl |
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name: Miracl (sw) |
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config: sw |
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split: dev |
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task: |
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type: Retrieval |
|
metrics: |
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- type: ndcg_at_1 |
|
value: 0.50415 |
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- type: ndcg_at_10 |
|
value: 0.59111 |
|
- type: ndcg_at_100 |
|
value: 0.64312 |
|
- type: ndcg_at_1000 |
|
value: 0.65089 |
|
- type: ndcg_at_20 |
|
value: 0.61651 |
|
- type: ndcg_at_3 |
|
value: 0.5304 |
|
- type: ndcg_at_5 |
|
value: 0.56139 |
|
- type: recall_at_1 |
|
value: 0.33267 |
|
- type: recall_at_10 |
|
value: 0.72082 |
|
- type: recall_at_100 |
|
value: 0.91377 |
|
- type: recall_at_1000 |
|
value: 0.96152 |
|
- type: recall_at_20 |
|
value: 0.79943 |
|
- type: recall_at_3 |
|
value: 0.5548 |
|
- type: recall_at_5 |
|
value: 0.64302 |
|
- dataset: |
|
type: miracl/mmteb-miracl |
|
name: Miracl (te) |
|
config: te |
|
split: dev |
|
task: |
|
type: Retrieval |
|
metrics: |
|
- type: ndcg_at_1 |
|
value: 0.64372 |
|
- type: ndcg_at_10 |
|
value: 0.78175 |
|
- type: ndcg_at_100 |
|
value: 0.79523 |
|
- type: ndcg_at_1000 |
|
value: 0.79774 |
|
- type: ndcg_at_20 |
|
value: 0.78826 |
|
- type: ndcg_at_3 |
|
value: 0.74856 |
|
- type: ndcg_at_5 |
|
value: 0.77128 |
|
- type: recall_at_1 |
|
value: 0.63688 |
|
- type: recall_at_10 |
|
value: 0.90358 |
|
- type: recall_at_100 |
|
value: 0.96558 |
|
- type: recall_at_1000 |
|
value: 0.9847 |
|
- type: recall_at_20 |
|
value: 0.92834 |
|
- type: recall_at_3 |
|
value: 0.81804 |
|
- type: recall_at_5 |
|
value: 0.87198 |
|
- dataset: |
|
type: miracl/mmteb-miracl |
|
name: Miracl (th) |
|
config: th |
|
split: dev |
|
task: |
|
type: Retrieval |
|
metrics: |
|
- type: ndcg_at_1 |
|
value: 0.65484 |
|
- type: ndcg_at_10 |
|
value: 0.71774 |
|
- type: ndcg_at_100 |
|
value: 0.75362 |
|
- type: ndcg_at_1000 |
|
value: 0.75898 |
|
- type: ndcg_at_20 |
|
value: 0.73709 |
|
- type: ndcg_at_3 |
|
value: 0.66199 |
|
- type: ndcg_at_5 |
|
value: 0.68451 |
|
- type: recall_at_1 |
|
value: 0.45911 |
|
- type: recall_at_10 |
|
value: 0.82619 |
|
- type: recall_at_100 |
|
value: 0.95515 |
|
- type: recall_at_1000 |
|
value: 0.98854 |
|
- type: recall_at_20 |
|
value: 0.88447 |
|
- type: recall_at_3 |
|
value: 0.67437 |
|
- type: recall_at_5 |
|
value: 0.73786 |
|
- dataset: |
|
type: miracl/mmteb-miracl |
|
name: Miracl (yo) |
|
config: yo |
|
split: dev |
|
task: |
|
type: Retrieval |
|
metrics: |
|
- type: ndcg_at_1 |
|
value: 0.46218 |
|
- type: ndcg_at_10 |
|
value: 0.64685 |
|
- type: ndcg_at_100 |
|
value: 0.66941 |
|
- type: ndcg_at_1000 |
|
value: 0.67361 |
|
- type: ndcg_at_20 |
|
value: 0.65548 |
|
- type: ndcg_at_3 |
|
value: 0.57609 |
|
- type: ndcg_at_5 |
|
value: 0.62021 |
|
- type: recall_at_1 |
|
value: 0.42787 |
|
- type: recall_at_10 |
|
value: 0.82913 |
|
- type: recall_at_100 |
|
value: 0.93277 |
|
- type: recall_at_1000 |
|
value: 0.96499 |
|
- type: recall_at_20 |
|
value: 0.85994 |
|
- type: recall_at_3 |
|
value: 0.65406 |
|
- type: recall_at_5 |
|
value: 0.7542 |
|
- dataset: |
|
type: miracl/mmteb-miracl |
|
name: Miracl (zh) |
|
config: zh |
|
split: dev |
|
task: |
|
type: Retrieval |
|
metrics: |
|
- type: ndcg_at_1 |
|
value: 0.41985 |
|
- type: ndcg_at_10 |
|
value: 0.4837 |
|
- type: ndcg_at_100 |
|
value: 0.55961 |
|
- type: ndcg_at_1000 |
|
value: 0.5762 |
|
- type: ndcg_at_20 |
|
value: 0.51595 |
|
- type: ndcg_at_3 |
|
value: 0.42094 |
|
- type: ndcg_at_5 |
|
value: 0.44273 |
|
- type: recall_at_1 |
|
value: 0.21446 |
|
- type: recall_at_10 |
|
value: 0.59695 |
|
- type: recall_at_100 |
|
value: 0.87388 |
|
- type: recall_at_1000 |
|
value: 0.96833 |
|
- type: recall_at_20 |
|
value: 0.69252 |
|
- type: recall_at_3 |
|
value: 0.40377 |
|
- type: recall_at_5 |
|
value: 0.4903 |
|
--- |
|
# Granite-Embedding-107m-multilingual |
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|
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**Model Summary:** |
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Granite-Embedding-107M-Multilingual is a 107M parameter dense biencoder embedding model from the Granite Embeddings suite that can be used to generate high quality text embeddings. This model produces embedding vectors of size 384 and is trained using a combination of open source relevance-pair datasets with permissive, enterprise-friendly license, and IBM collected and generated datasets. This model is developed using contrastive finetuning, knowledge distillation and model merging for improved performance. |
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- **Developers:** Granite Embedding Team, IBM |
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- **GitHub Repository:** [ibm-granite/granite-embedding-models](https://github.com/ibm-granite/granite-embedding-models) |
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- **Website**: [Granite Docs](https://www.ibm.com/granite/docs/) |
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- **Paper:** Coming Soon |
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- **Release Date**: December 18th, 2024 |
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- **License:** [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0) |
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**Supported Languages:** |
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English, German, Spanish, French, Japanese, Portuguese, Arabic, Czech, Italian, Korean, Dutch, and Chinese. Users may finetune Granite-Embedding-107M-Multilingual for languages beyond these 12 languages. |
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**Intended use:** |
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The model is designed to produce fixed length vector representations for a given text, which can be used for text similarity, retrieval, and search applications. |
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**Usage with Sentence Transformers:** |
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The model is compatible with SentenceTransformer library and is very easy to use: |
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First, install the sentence transformers library |
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```shell |
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pip install sentence_transformers |
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``` |
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The model can then be used to encode pairs of text and find the similarity between their representations |
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|
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```python |
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from sentence_transformers import SentenceTransformer, util |
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model_path = "ibm-granite/granite-embedding-107m-multilingual" |
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# Load the Sentence Transformer model |
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model = SentenceTransformer(model_path) |
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input_queries = [ |
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' Who made the song My achy breaky heart? ', |
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'summit define' |
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] |
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input_passages = [ |
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"Achy Breaky Heart is a country song written by Don Von Tress. Originally titled Don't Tell My Heart and performed by The Marcy Brothers in 1991. ", |
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"Definition of summit for English Language Learners. : 1 the highest point of a mountain : the top of a mountain. : 2 the highest level. : 3 a meeting or series of meetings between the leaders of two or more governments." |
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] |
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# encode queries and passages |
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query_embeddings = model.encode(input_queries) |
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passage_embeddings = model.encode(input_passages) |
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# calculate cosine similarity |
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print(util.cos_sim(query_embeddings, passage_embeddings)) |
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``` |
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**Usage with Huggingface Transformers:** |
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This is a simple example of how to use the Granite-Embedding-107m-Multilingual model with the Transformers library and PyTorch. |
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First, install the required libraries |
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```shell |
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pip install transformers torch |
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``` |
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The model can then be used to encode pairs of text |
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|
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```python |
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import torch |
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from transformers import AutoModel, AutoTokenizer |
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model_path = "ibm-granite/granite-embedding-107m-multilingual" |
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# Load the model and tokenizer |
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model = AutoModel.from_pretrained(model_path) |
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tokenizer = AutoTokenizer.from_pretrained(model_path) |
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model.eval() |
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input_queries = [ |
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' Who made the song My achy breaky heart? ', |
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'summit define' |
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] |
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|
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# tokenize inputs |
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tokenized_queries = tokenizer(input_queries, padding=True, truncation=True, return_tensors='pt') |
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# encode queries |
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with torch.no_grad(): |
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# Queries |
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model_output = model(**tokenized_queries) |
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# Perform pooling. granite-embedding-30m-english uses CLS Pooling |
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query_embeddings = model_output[0][:, 0] |
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# normalize the embeddings |
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query_embeddings = torch.nn.functional.normalize(query_embeddings, dim=1) |
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``` |
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**Evaluation:** |
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The average performance of the Granite-Embedding-107M-Multilingual on Multilingual Miracl (across 18 langauges), Mintaka Retrieval (across 8 languages) and MTEB Retrieval for English (across 15 tasks), German (across 4 tasks), Spanish (across 2 tasks), Frenc (across 5 tasks), Japanese (across 2 tasks), Arabic (1 task), Korean (1 task) and Chinese (across 8 tasks) is reported below. Granite-Embedding-107M-Multilingual is twice as fast as other models with similar embedding dimensions. |
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| Model | Paramters (M)| Embedding Dimension | Miracl (18) | Mintaka Retrieval (8) | MTEB English (15) | MTEB German (4) |MTEB Spanish (2) | MTEB French (5) | MTEB Japanese (2) | MTEB Arabic (1) | MTEB Korean (1) | MTEB Chinese (8) | |
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|------------------------------------|:------------:|:-------------------:|:-------------:| :---------------------:|:-----------------:|:---------------:|:---------------:|:---------------:|:----------------:|:----------------:|----------------:|-----------------:| |
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|granite-embedding-107m-multilingual | 107 | 384 | 55.9 | 22.6 | 45.3 | 70.3 | 48.7 | 51.1 | 59.0 | 63.2 | 70.5 | 40.8 | |
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**Model Architecture:** |
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Granite-Embedding-107m-Multilingual is based on an encoder-only XLM-RoBERTa like transformer architecture, trained internally at IBM Research. |
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| Model | granite-embedding-30m-english | granite-embedding-125m-english | granite-embedding-107m-multilingual | granite-embedding-278m-multilingual | |
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| :--------- | :-------:| :--------: | :---------:| :-----:| |
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| Embedding size | 384 | 768 | **384** | 768 | |
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| Number of layers | 6 | 12 | **6** | 12 | |
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| Number of attention heads | 12 | 12 | **12** | 12 | |
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| Intermediate size | 1536 | 3072 | **1536** | 3072 | |
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| Activation Function | GeLU | GeLU | **GeLU** | GeLU | |
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| Vocabulary Size | 50265 | 50265 | **250002** | 250002 | |
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| Max. Sequence Length | 512 | 512 | **512** | 512 | |
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| # Parameters | 30M | 125M | **107M** | 278M | |
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**Training Data:** |
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Overall, the training data consists of four key sources: (1) unsupervised title-body paired data scraped from the web, (2) publicly available paired with permissive, enterprise-friendly license, (3) IBM-internal paired data targetting specific technical domains, and (4) IBM-generated synthetic data. The data is listed below: |
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|
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| **Dataset** | **Num. Pairs** | |
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|:--------------------------------------------------------------------------|:--------------:| |
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| Multilingual MC4 | 52,823,484 | |
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| Multilingual Webhose | 12,369,322 | |
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| English Wikipedia | 20,745,403 | |
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| Multilingual Wikimedia | 2,911,090 | |
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| Miracl Corpus (Title-Body) | 10,120,398 | |
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| Stack Exchange Duplicate questions (titles) | 304,525 | |
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| Stack Exchange Duplicate questions (titles) | 304,525 | |
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| Stack Exchange Duplicate questions (bodies) | 250,519 | |
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| Machine Translations of Stack Exchange Duplicate questions (titles) | 187,195 | |
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| Stack Exchange (Title, Answer) pairs | 4,067,139 | |
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| Stack Exchange (Title, Body) pairs | 23,978,013 | |
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| Stack Exchange (Title, Body) pairs | 23,978,013 | |
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| Machine Translations of Stack Exchange (Title+Body, Answer) pairs | 1,827,15 | |
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| SearchQA | 582,261 | |
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| S2ORC (Title, Abstract) | 41,769,185 | |
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| WikiAnswers Duplicate question pairs | 77,427,422 | |
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| CCNews | 614,664 | |
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| XSum | 226,711 | |
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| SimpleWiki | 102,225 | |
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| Machine Translated Cross Lingual Parallel Corpora | 28,376,115 | |
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| SPECTER citation triplets | 684,100 | |
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| Machine Translations of SPECTER citation triplets | 4,104,600 | |
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| Natural Questions (NQ) | 100,231 | |
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| SQuAD2.0 | 87,599 | |
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| HotpotQA | 85,000 | |
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| Fever | 109,810 | |
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| PubMed | 20,000,000 | |
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| Multilingual Miracl Triples | 81,409 | |
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| Multilingual MrTydi Triples | 48,715 | |
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| Sadeeem Question Asnwering | 4,037 | |
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| DBPedia Title-Body Pairs | 4,635,922 | |
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| Synthetic: English Query-Wikipedia Passage | 1,879,093 | |
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| Synthetic: English Fact Verification | 9,888 | |
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| Synthetic: Multilingual Query-Wikipedia Passage | 300,266 | |
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| Synthetic: Multilingual News Summaries | 37,489 | |
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| IBM Internal Triples | 40,290 | |
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| IBM Internal Title-Body Pairs | 1,524,586 | |
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|
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Notably, we do not use the popular MS-MARCO retrieval dataset in our training corpus due to its non-commercial license, while other open-source models train on this dataset due to its high quality. |
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|
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**Infrastructure:** |
|
We train Granite Embedding Models using IBM's computing cluster, Cognitive Compute Cluster, which is outfitted with NVIDIA A100 80gb GPUs. This cluster provides a scalable and efficient infrastructure for training our models over multiple GPUs. |
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|
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**Ethical Considerations and Limitations:** |
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The data used to train the base language model was filtered to remove text containing hate, abuse, and profanity. Granite-Embedding-278m-Multilingual is trained only for English texts, and has a context length of 512 tokens (longer texts will be truncated to this size). |
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|
|
<!-- ## Citation |
|
``` |
|
@misc{granite-embedding-models, |
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author = {author 1, author2, ...}, |
|
title = {}, |
|
journal = {}, |
|
volume = {}, |
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year = {2024}, |
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url = {https://arxiv.org/abs/0000.00000}, |
|
} |
|
``` --> |
|
|
