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--- |
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pipeline_tag: sentence-similarity |
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tags: |
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- sentence-transformers |
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- feature-extraction |
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- sentence-similarity |
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- transformers |
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- information-retrieval |
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language: pl |
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license: apache-2.0 |
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widget: |
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- source_sentence: "query: Jak dożyć 100 lat?" |
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sentences: |
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- "passage: Trzeba zdrowo się odżywiać i uprawiać sport." |
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- "passage: Trzeba pić alkohol, imprezować i jeździć szybkimi autami." |
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- "passage: Gdy trwała kampania politycy zapewniali, że rozprawią się z zakazem niedzielnego handlu." |
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--- |
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<h1 align="center">MMLW-retrieval-e5-large</h1> |
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MMLW (muszę mieć lepszą wiadomość) are neural text encoders for Polish. |
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This model is optimized for information retrieval tasks. It can transform queries and passages to 1024 dimensional vectors. |
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The model was developed using a two-step procedure: |
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- In the first step, it was initialized with multilingual E5 checkpoint, and then trained with [multilingual knowledge distillation method](https://aclanthology.org/2020.emnlp-main.365/) on a diverse corpus of 60 million Polish-English text pairs. We utilised [English FlagEmbeddings (BGE)](https://huggingface.co/BAAI/bge-large-en) as teacher models for distillation. |
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- The second step involved fine-tuning the obtained models with contrastrive loss on [Polish MS MARCO](https://huggingface.co/datasets/clarin-knext/msmarco-pl) training split. In order to improve the efficiency of contrastive training, we used large batch sizes - 1152 for small, 768 for base, and 288 for large models. Fine-tuning was conducted on a cluster of 12 A100 GPUs. |
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⚠️ **2023-12-26:** We have updated the model to a new version with improved results. You can still download the previous version using the **v1** tag: `AutoModel.from_pretrained("sdadas/mmlw-retrieval-e5-large", revision="v1")` ⚠️ |
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## Usage (Sentence-Transformers) |
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⚠️ Our dense retrievers require the use of specific prefixes and suffixes when encoding texts. For this model, queries should be prefixed with **"query: "** and passages with **"passage: "** ⚠️ |
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You can use the model like this with [sentence-transformers](https://www.SBERT.net): |
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```python |
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from sentence_transformers import SentenceTransformer |
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from sentence_transformers.util import cos_sim |
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query_prefix = "query: " |
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answer_prefix = "passage: " |
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queries = [query_prefix + "Jak dożyć 100 lat?"] |
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answers = [ |
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answer_prefix + "Trzeba zdrowo się odżywiać i uprawiać sport.", |
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answer_prefix + "Trzeba pić alkohol, imprezować i jeździć szybkimi autami.", |
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answer_prefix + "Gdy trwała kampania politycy zapewniali, że rozprawią się z zakazem niedzielnego handlu." |
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] |
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model = SentenceTransformer("sdadas/mmlw-retrieval-e5-large") |
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queries_emb = model.encode(queries, convert_to_tensor=True, show_progress_bar=False) |
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answers_emb = model.encode(answers, convert_to_tensor=True, show_progress_bar=False) |
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best_answer = cos_sim(queries_emb, answers_emb).argmax().item() |
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print(answers[best_answer]) |
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# Trzeba zdrowo się odżywiać i uprawiać sport. |
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``` |
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## Evaluation Results |
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The model achieves **NDCG@10** of **58.30** on the Polish Information Retrieval Benchmark. See [PIRB Leaderboard](https://huggingface.co/spaces/sdadas/pirb) for detailed results. |
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## Acknowledgements |
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This model was trained with the A100 GPU cluster support delivered by the Gdansk University of Technology within the TASK center initiative. |