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--- |
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license: mit |
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language: |
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- en |
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tags: |
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- sparse sparsity quantized onnx embeddings int8 |
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model-index: |
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- name: bge-base-en-v1.5-sparse |
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results: |
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- task: |
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type: Classification |
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dataset: |
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type: mteb/amazon_counterfactual |
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name: MTEB AmazonCounterfactualClassification (en) |
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config: en |
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split: test |
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revision: e8379541af4e31359cca9fbcf4b00f2671dba205 |
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metrics: |
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- type: accuracy |
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value: 75.38805970149254 |
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- type: ap |
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value: 38.80643435437097 |
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- type: f1 |
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value: 69.52906891019036 |
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- task: |
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type: Classification |
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dataset: |
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type: mteb/amazon_polarity |
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name: MTEB AmazonPolarityClassification |
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config: default |
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split: test |
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revision: e2d317d38cd51312af73b3d32a06d1a08b442046 |
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metrics: |
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- type: accuracy |
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value: 90.72759999999998 |
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- type: ap |
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value: 87.07910150764239 |
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- type: f1 |
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value: 90.71025910882096 |
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- task: |
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type: Classification |
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dataset: |
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type: mteb/amazon_reviews_multi |
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name: MTEB AmazonReviewsClassification (en) |
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config: en |
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split: test |
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revision: 1399c76144fd37290681b995c656ef9b2e06e26d |
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metrics: |
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- type: accuracy |
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value: 45.494 |
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- type: f1 |
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value: 44.917953161904805 |
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- task: |
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type: Classification |
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dataset: |
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type: mteb/banking77 |
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name: MTEB Banking77Classification |
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config: default |
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split: test |
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revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 |
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metrics: |
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- type: accuracy |
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value: 84.29545454545455 |
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- type: f1 |
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value: 84.26780483160312 |
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- task: |
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type: Classification |
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dataset: |
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type: mteb/emotion |
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name: MTEB EmotionClassification |
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config: default |
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split: test |
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revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 |
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metrics: |
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- type: accuracy |
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value: 46.705 |
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- type: f1 |
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value: 41.82618717355017 |
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- task: |
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type: Classification |
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dataset: |
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type: mteb/imdb |
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name: MTEB ImdbClassification |
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config: default |
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split: test |
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revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 |
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metrics: |
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- type: accuracy |
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value: 83.14760000000001 |
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- type: ap |
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value: 77.40813245635195 |
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- type: f1 |
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value: 83.08648833100911 |
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- task: |
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type: Classification |
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dataset: |
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type: mteb/mtop_domain |
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name: MTEB MTOPDomainClassification (en) |
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config: en |
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split: test |
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revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf |
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metrics: |
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- type: accuracy |
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value: 92.0519835841313 |
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- type: f1 |
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value: 91.73392170858916 |
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- task: |
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type: Classification |
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dataset: |
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type: mteb/mtop_intent |
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name: MTEB MTOPIntentClassification (en) |
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config: en |
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split: test |
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revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba |
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metrics: |
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- type: accuracy |
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value: 72.48974008207935 |
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- type: f1 |
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value: 54.812872972777505 |
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- task: |
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type: Classification |
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dataset: |
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type: mteb/amazon_massive_intent |
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name: MTEB MassiveIntentClassification (en) |
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config: en |
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split: test |
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revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
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metrics: |
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- type: accuracy |
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value: 73.17753866846 |
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- type: f1 |
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value: 71.51091282373878 |
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- task: |
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type: Classification |
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dataset: |
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type: mteb/amazon_massive_scenario |
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name: MTEB MassiveScenarioClassification (en) |
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config: en |
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split: test |
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revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
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metrics: |
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- type: accuracy |
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value: 77.5353059852051 |
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- type: f1 |
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value: 77.42427561340143 |
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- task: |
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type: Classification |
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dataset: |
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type: mteb/toxic_conversations_50k |
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name: MTEB ToxicConversationsClassification |
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config: default |
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split: test |
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revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c |
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metrics: |
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- type: accuracy |
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value: 70.917 |
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- type: ap |
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value: 13.760770628090576 |
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- type: f1 |
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value: 54.23887489664618 |
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- task: |
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type: Classification |
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dataset: |
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type: mteb/tweet_sentiment_extraction |
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name: MTEB TweetSentimentExtractionClassification |
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config: default |
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split: test |
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revision: d604517c81ca91fe16a244d1248fc021f9ecee7a |
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metrics: |
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- type: accuracy |
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value: 59.49349179400113 |
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- type: f1 |
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value: 59.815392064510775 |
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--- |
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This is the sparsified ONNX variant of the [bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) embeddings model created with [DeepSparse Optimum](https://github.com/neuralmagic/optimum-deepsparse) for ONNX export/inference pipeline and Neural Magic's [Sparsify](https://github.com/neuralmagic/sparsify) for one-shot quantization (INT8) and unstructured pruning (50%). |
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Current list of sparse and quantized bge ONNX models: |
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| Links | Sparsification Method | |
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| --------------------------------------------------------------------------------------------------- | ---------------------- | |
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| [zeroshot/bge-large-en-v1.5-sparse](https://huggingface.co/zeroshot/bge-large-en-v1.5-sparse) | Quantization (INT8) & 50% Pruning | |
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| [zeroshot/bge-large-en-v1.5-quant](https://huggingface.co/zeroshot/bge-large-en-v1.5-quant) | Quantization (INT8) | |
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| [zeroshot/bge-base-en-v1.5-sparse](https://huggingface.co/zeroshot/bge-base-en-v1.5-sparse) | Quantization (INT8) & 50% Pruning | |
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| [zeroshot/bge-base-en-v1.5-quant](https://huggingface.co/zeroshot/bge-base-en-v1.5-quant) | Quantization (INT8) | |
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| [zeroshot/bge-small-en-v1.5-sparse](https://huggingface.co/zeroshot/bge-small-en-v1.5-sparse) | Quantization (INT8) & 50% Pruning | |
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| [zeroshot/bge-small-en-v1.5-quant](https://huggingface.co/zeroshot/bge-small-en-v1.5-quant) | Quantization (INT8) | |
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For general questions on these models and sparsification methods, reach out to the engineering team on our [community Slack](https://join.slack.com/t/discuss-neuralmagic/shared_invite/zt-q1a1cnvo-YBoICSIw3L1dmQpjBeDurQ). |
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