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Add model
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README.md
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library_name: xpmir
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---
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# SPLADE_DistilMSE: SPLADEv2 trained with the distillated triplets
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Training data from: https://github.com/sebastian-hofstaetter/neural-ranking-kd
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From Distillation to Hard Negative Sampling: Making Sparse Neural IR Models
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More Effective (Thibault Formal, Carlos Lassance, Benjamin Piwowarski,
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Stéphane Clinchant). 2022. https://arxiv.org/abs/2205.04733
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## Using the model)
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The model can be loaded with [experimaestro IR](https://experimaestro-ir.readthedocs.io/en/latest/)
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```py
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from xpmir.models import AutoModel
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# Model that can be re-used in experiments
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model = AutoModel.load_from_hf_hub("xpmir/SPLADE_DistilMSE")
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model
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```
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## Results
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| Dataset | AP | P@20 | RR | RR@10 | nDCG | nDCG@10 | nDCG@20 |
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|----| ---|------|------|------|------|------|------|
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| trec2019 | 0.5102 | 0.7360 | 0.9612 | 0.9612 | 0.7407 | 0.7300 | 0.7097 |
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library_name: xpmir
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---
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# SPLADE_DistilMSE: SPLADEv2 trained with the distillated triplets
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Training data from: https://github.com/sebastian-hofstaetter/neural-ranking-kd
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From Distillation to Hard Negative Sampling: Making Sparse Neural IR Models
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More Effective (Thibault Formal, Carlos Lassance, Benjamin Piwowarski,
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Stéphane Clinchant). 2022. https://arxiv.org/abs/2205.04733
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## Using the model
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The model can be loaded with [experimaestro
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IR](https://experimaestro-ir.readthedocs.io/en/latest/)
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```py from xpmir.models import AutoModel
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# Model that can be re-used in experiments model =
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AutoModel.load_from_hf_hub("xpmir/SPLADE_DistilMSE")
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# Use this if you want to actually use the model model =
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AutoModel.load_from_hf_hub("xpmir/SPLADE_DistilMSE", as_instance=True)
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model.initialize() model.rsv("walgreens store sales average", "The average
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Walgreens salary ranges...")
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```
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## Results
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| Dataset | AP | P@20 | RR | RR@10 | nDCG | nDCG@10 | nDCG@20 |
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|----| ---|------|------|------|------|------|------|
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| trec2019 | 0.5102 | 0.7360 | 0.9612 | 0.9612 | 0.7407 | 0.7300 | 0.7097 |
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definition.json
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[{"id":
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[{"id": 140703274169536, "module": "xpmir.text.huggingface", "type": "TransformerTokensEncoder", "typename": "xpmir.text.huggingface.TransformerTokensEncoder", "identifier": "d26c7c4d00bb2002f07f80036294ed81355c1256fde3758c6ab9451c7fc06c2c", "fields": {"model_id": "distilbert-base-uncased", "trainable": true, "layer": 0, "dropout": 0.0}}, {"id": 140703274169104, "module": "xpmir.neural.splade", "type": "MaxAggregation", "typename": "xpmir.neural.splade.MaxAggregation", "identifier": "6d85ac05decb4f139354ac3f79990a2afdb8ced21c8861f2dd5c720484514f48", "fields": {}}, {"id": 140703274169824, "module": "xpmir.neural.splade", "type": "SpladeTextEncoder", "typename": "xpmir.neural.splade.SpladeTextEncoder", "identifier": "9f455ba2eb9c3dc5ce40d4f0620b24a8d086b1a35449380af6cf625a69f2efeb", "fields": {"encoder": {"type": "python", "value": 140703274169536}, "aggregation": {"type": "python", "value": 140703274169104}, "maxlen": 200}}, {"id": 140703274171744, "module": "xpmir.neural.splade", "type": "SpladeTextEncoder", "typename": "xpmir.neural.splade.SpladeTextEncoder", "identifier": "af02fa8dcdc136f3c02eeb124fe524302c85d736deeaa6d04b3acc215d1593ac", "fields": {"encoder": {"type": "python", "value": 140703274169536}, "aggregation": {"type": "python", "value": 140703274169104}, "maxlen": 30}}, {"id": 140703274334768, "module": "xpmir.neural.dual", "type": "DotDense", "typename": "xpmir.neural.dual.DotDense", "identifier": "d4e16bd0471b8b48c996614c52a927c911296acf20fca61b75b431499cf47b47", "fields": {"checkpoint": {"type": "path.serialized", "value": "checkpoint", "is_folder": true}, "encoder": {"type": "python", "value": 140703274169824}, "query_encoder": {"type": "python", "value": 140703274171744}}}]
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runs/splade_max-bert_hard_negative-RR@10/1675450725.7928324/events.out.tfevents.1675450725.aerosmith.82538.1
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size 584
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runs/splade_max-bert_hard_negative-RR@10/events.out.tfevents.1675334263.aerosmith.82538.0
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version https://git-lfs.github.com/spec/v1
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