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1_Pooling/config.json

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+{
+  "word_embedding_dimension": 768,
+  "pooling_mode_cls_token": true,
+  "pooling_mode_mean_tokens": false,
+  "pooling_mode_max_tokens": false,
+  "pooling_mode_mean_sqrt_len_tokens": false
+}

README.md

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+---
+pipeline_tag: sentence-similarity
+language:
+- pl
+tags:
+- sentence-transformers
+- feature-extraction
+- sentence-similarity
+- transformers
+datasets:
+- ipipan/polqa
+- ipipan/maupqa
+license: cc-by-sa-4.0
+widget:
+- source_sentence: "Pytanie: W jakim mieście urodził się Zbigniew Herbert?"
+  sentences:
+    - "Zbigniew Herbert</s>Zbigniew Bolesław Ryszard Herbert (ur. 29 października 1924 we Lwowie, zm. 28 lipca 1998 w Warszawie) – polski poeta, eseista i dramaturg."
+    - "Zbigniew Herbert</s>Lato 1968 Herbert spędził w USA (na zaproszenie Poetry Center)."
+    - "Herbert George Wells</s>Herbert George Wells (ur. 21 września 1866 w Bromley, zm. 13 sierpnia 1946 w Londynie) – brytyjski pisarz i biolog."
+  example_title: "Zbigniew Herbert"
+---
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/5eb2c5ef4e876668a0c3779e/j2JE7_VnbRifCmV7_4BP9.png)
+
+# Silver Retriever Base (v1.1)
+
+Silver Retriever model encodes the Polish sentences or paragraphs into a 768-dimensional dense vector space and can be used for tasks like document retrieval or semantic search.
+
+It was initialized from the [HerBERT-base](https://huggingface.co/allegro/herbert-base-cased) model and fine-tuned on the [PolQA](https://huggingface.co/ipipan/polqa) and [MAUPQA](https://huggingface.co/ipipan/maupqa) datasets for 8,000 steps with a batch size of 8,192. Please refer to the [SilverRetriever: Advancing Neural Passage Retrieval for Polish Question Answering](https://arxiv.org/abs/2309.08469) for more details.
+
+## Evaluation
+
+
+| **Model**           | **Average [Acc]** | **Average [NDCG]** | [**PolQA**](https://huggingface.co/datasets/ipipan/polqa) **[Acc]** | [**PolQA**](https://huggingface.co/datasets/ipipan/polqa) **[NDCG]** | [**Allegro FAQ**](https://huggingface.co/datasets/piotr-rybak/allegro-faq) **[Acc]** | [**Allegro FAQ**](https://huggingface.co/datasets/piotr-rybak/allegro-faq) **[NDCG]** | [**Legal Questions**](https://huggingface.co/datasets/piotr-rybak/legal-questions) **[Acc]** | [**Legal Questions**](https://huggingface.co/datasets/piotr-rybak/legal-questions) **[NDCG]** |
+|--------------------:|------------:|-------------:|------------:|-------------:|------------:|-------------:|------------:|-------------:|
+| BM25                | 74.87       | 51.81        | 61.35       | 24.51        | 66.89       | 48.71        | 96.38   | **82.21**    |
+| BM25 (lemma)        | 80.46       | 55.44        | 71.49       | 31.97        | 75.33       | 55.70        | 94.57       | 78.65        |
+| [MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2)       | 62.62       | 39.21        | 37.24       | 11.93        | 71.67       | 51.25        | 78.97       | 54.44        |
+| [LaBSE](https://huggingface.co/sentence-transformers/LaBSE)               | 64.89       | 39.47        | 46.23       | 15.53        | 67.11       | 46.71        | 81.34       | 56.16        |
+| [mContriever-Base](https://huggingface.co/nthakur/mcontriever-base-msmarco)    | 86.31       | 60.37        | 78.66       | 36.30        | 84.44       | 67.38        | 95.82       | 77.42        |
+| [E5-Base](https://huggingface.co/intfloat/multilingual-e5-base)             | 91.58       | 66.56        | 86.61       | **46.08**    | 91.89       | 75.90        | 96.24       | 77.69        |
+| [ST-DistilRoBERTa](https://huggingface.co/sdadas/st-polish-paraphrase-from-distilroberta)    | 73.78       | 48.29        | 48.43       | 16.73        | 84.89       | 64.39        | 88.02       | 63.76        |
+| [ST-MPNet](https://huggingface.co/sdadas/st-polish-paraphrase-from-mpnet)          | 76.66       | 49.99        | 56.80       | 21.55        | 86.00       | 65.44        | 87.19       | 62.99        |
+| [HerBERT-QA](https://huggingface.co/ipipan/herbert-base-qa-v1)          | 84.23       | 54.36        | 75.84       | 32.52        | 85.78       | 63.58        | 91.09       | 66.99        |
+| [Silver Retriever v1](https://huggingface.co/ipipan/silver-retriever-base-v1) | 92.45   | 66.72    | 87.24   | 43.40        | **94.56**   | 79.66    | 95.54       | 77.10        |
+| [Silver Retriever v1.1](https://huggingface.co/ipipan/silver-retriever-base-v1.1) | **93.18**   | **67.55**    | **88.60**   | 44.88        | 94.00   | **79.83**    | **96.94**       | 77.95        |
+
+Legend:
+- **Acc** is the Accuracy at 10
+- **NDCG** is the Normalized Discounted Cumulative Gain at 10
+
+
+## Usage
+
+### Preparing inputs
+
+The model was trained on question-passage pairs and works best when the input is the same format as that used during training:
+- We added the phrase `Pytanie:` to the beginning of the question.
+- The training passages consisted of `title` and `text` concatenated with the special token `</s>`. Even if your passages don't have a `title`, it is still beneficial to prefix a passage with the `</s>` token.
+- Although we used the dot product during training, the model usually works better with the cosine distance.
+
+### Inference with Sentence-Transformers
+
+Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
+
+```
+pip install -U sentence-transformers
+```
+
+Then you can use the model like this:
+
+```python
+from sentence_transformers import SentenceTransformer
+sentences = [
+    "Pytanie: W jakim mieście urodził się Zbigniew Herbert?", 
+    "Zbigniew Herbert</s>Zbigniew Bolesław Ryszard Herbert (ur. 29 października 1924 we Lwowie, zm. 28 lipca 1998 w Warszawie) – polski poeta, eseista i dramaturg.",
+]
+
+model = SentenceTransformer('ipipan/silver-retriever-base-v1.1')
+embeddings = model.encode(sentences)
+print(embeddings)
+```
+
+### Inference with HuggingFace Transformers
+Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
+
+```python
+from transformers import AutoTokenizer, AutoModel
+import torch
+
+
+def cls_pooling(model_output, attention_mask):
+    return model_output[0][:,0]
+
+
+# Sentences we want sentence embeddings for
+sentences = [
+    "Pytanie: W jakim mieście urodził się Zbigniew Herbert?", 
+    "Zbigniew Herbert</s>Zbigniew Bolesław Ryszard Herbert (ur. 29 października 1924 we Lwowie, zm. 28 lipca 1998 w Warszawie) – polski poeta, eseista i dramaturg.",
+]
+# Load model from HuggingFace Hub
+tokenizer = AutoTokenizer.from_pretrained('ipipan/silver-retriever-base-v1.1')
+model = AutoModel.from_pretrained('ipipan/silver-retriever-base-v1.1')
+
+# Tokenize sentences
+encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
+
+# Compute token embeddings
+with torch.no_grad():
+    model_output = model(**encoded_input)
+
+# Perform pooling. In this case, cls pooling.
+sentence_embeddings = cls_pooling(model_output, encoded_input['attention_mask'])
+
+print("Sentence embeddings:")
+print(sentence_embeddings)
+```
+
+## Full Model Architecture
+```
+SentenceTransformer(
+  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
+  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
+)
+```
+
+## Additional Information
+
+### Model Creators
+
+The model was created by Piotr Rybak from the [Institute of Computer Science, Polish Academy of Sciences](http://zil.ipipan.waw.pl/).
+
+This work was supported by the European Regional Development Fund as a part of 2014–2020 Smart Growth Operational Programme, CLARIN — Common Language Resources and Technology Infrastructure, project no. POIR.04.02.00-00C002/19.
+
+### Licensing Information
+
+CC BY-SA 4.0
+
+### Citation Information
+
+```
+@misc{rybak2023silverretriever,
+    title={SilverRetriever: Advancing Neural Passage Retrieval for Polish Question Answering}, 
+    author={Piotr Rybak and Maciej Ogrodniczuk},
+    year={2023},
+    eprint={2309.08469},
+    archivePrefix={arXiv},
+    primaryClass={cs.CL}
+}
+```

config.json

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+{
+  "_name_or_path": "../../../sellaservice/rt/model_all_filter5p_bs256_lr2e5_40k/",
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "LABEL_0"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "LABEL_0": 0
+  },
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 514,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 1,
+  "position_embedding_type": "absolute",
+  "tokenizer_class": "HerbertTokenizerFast",
+  "torch_dtype": "float32",
+  "transformers_version": "4.30.1",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 50000
+}

config_sentence_transformers.json

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+{
+  "__version__": {
+    "sentence_transformers": "2.2.2",
+    "transformers": "4.30.1",
+    "pytorch": "2.0.1"
+  }
+}

modules.json

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+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  }
+]

pytorch_model.bin

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sentence_bert_config.json

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+{
+  "max_seq_length": 512,
+  "do_lower_case": false
+}

special_tokens_map.json

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+{
+  "bos_token": "<s>",
+  "cls_token": "<s>",
+  "mask_token": "<mask>",
+  "pad_token": "<pad>",
+  "sep_token": "</s>",
+  "unk_token": "<unk>"
+}

tokenizer_config.json

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+{
+  "additional_special_tokens": [],
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "<s>",
+  "do_lowercase_and_remove_accent": false,
+  "id2lang": null,
+  "lang2id": null,
+  "mask_token": "<mask>",
+  "model_max_length": 512,
+  "pad_token": "<pad>",
+  "sep_token": "</s>",
+  "tokenizer_class": "HerbertTokenizer",
+  "unk_token": "<unk>"
+}