Add new SentenceTransformer model.

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "word_embedding_dimension": 384,
+  "pooling_mode_cls_token": true,
+  "pooling_mode_mean_tokens": false,
+  "pooling_mode_max_tokens": false,
+  "pooling_mode_mean_sqrt_len_tokens": false,
+  "pooling_mode_weightedmean_tokens": false,
+  "pooling_mode_lasttoken": false,
+  "include_prompt": true
+}

README.md

@@ -0,0 +1,952 @@
+---
+base_model: BAAI/bge-small-en
+datasets: []
+language: []
+library_name: sentence-transformers
+metrics:
+- cosine_accuracy@1
+- cosine_accuracy@3
+- cosine_accuracy@5
+- cosine_accuracy@10
+- cosine_precision@1
+- cosine_precision@3
+- cosine_precision@5
+- cosine_precision@10
+- cosine_recall@1
+- cosine_recall@3
+- cosine_recall@5
+- cosine_recall@10
+- cosine_ndcg@10
+- cosine_mrr@10
+- cosine_map@100
+- dot_accuracy@1
+- dot_accuracy@3
+- dot_accuracy@5
+- dot_accuracy@10
+- dot_precision@1
+- dot_precision@3
+- dot_precision@5
+- dot_precision@10
+- dot_recall@1
+- dot_recall@3
+- dot_recall@5
+- dot_recall@10
+- dot_ndcg@10
+- dot_mrr@10
+- dot_map@100
+pipeline_tag: sentence-similarity
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:1010
+- loss:MultipleNegativesRankingLoss
+widget:
+- source_sentence: How does Prompt-RAG differ from traditional vector embedding-based
+    methodologies?
+  sentences:
+  - Prompt-RAG differs from traditional vector embedding-based methodologies by adopting
+    a more direct and flexible retrieval process based on natural language prompts,
+    eliminating the need for a vector database or an algorithm for indexing and selecting
+    vectors.
+  - By introducing a pre-aligned phrase prior to the standard SFT stage, LLMs are
+    guided to concentrate on the aligned knowledge, thereby unlocking their internal
+    alignment abilities and improving their performance.
+  - The accuracy of GPT 3.5 on 2500 overall TeleQnA questions related to 3GPP documents
+    is 60.1, while the accuracy of GPT 3.5 + Telco-RAG is 6.9 points higher.
+- source_sentence: Explain the concept of in-context learning as described in the
+    paper 'An explanation of in-context learning as implicit Bayesian inference'.
+  sentences:
+  - The main theme of the paper is that language models can learn to perform many
+    tasks in a zero-shot setting, without any explicit supervision.
+  - In-context learning, as explained in the paper, is a process where a language
+    model uses the context provided in the input to make predictions or generate outputs
+    without explicit training on the specific task. The paper argues that this process
+    can be understood as an implicit form of Bayesian inference.
+  - The paper was presented in the 55th Annual Meeting of the Association for Computational
+    Linguistics.
+- source_sentence: What is the purpose of the survey conducted by Huang et al. (2023)?
+  sentences:
+  - The purpose of the survey conducted by Huang et al. (2023) is to provide a comprehensive
+    overview of hallucination in large language models, including its principles,
+    taxonomy, challenges, and open questions.
+  - The study of Human and American Translation Learning contributes to language development
+    by understanding the cognitive processes involved in translating between languages,
+    which can lead to improved teaching methods and translation technology.
+  - Using profile data, triplet examples are constructed in the format of (𝑥𝑖, 𝑥 𝑖−,
+    𝑥 𝑖+). The anchor example 𝑥𝑖 is constructed as the combination of the content
+    𝑐𝑖 and the corresponding label 𝑙𝑖.
+- source_sentence: Who is the first author of the paper and what is their last name?
+  sentences:
+  - The key findings are that Vul-RAG achieves the highest accuracy and pairwise accuracy
+    among all baselines, substantially outperforming the best baseline LLMAO. It also
+    achieves the best trade-off between recall and precision.
+  - The first author of the paper is Nandan Thakur. Their last name is Thakur.
+  - The paper was presented at the 2022 Conference on Empirical Methods in Natural
+    Language Processing (EMNLP).
+- source_sentence: Compare the top-5 retrieval accuracy of BM25 + MQ and SERM + BF
+    for the NQ Dataset and HotpotQA.
+  sentences:
+  - For the NQ Dataset, SERM + BF has a top-5 retrieval accuracy of 88.22, which is
+    significantly higher than BM25 + MQ's accuracy of 25.19. For HotpotQA, SERM +
+    BF was not tested, but BM25 + MQ has a top-5 retrieval accuracy of 49.52.
+  - The paper was presented at the 17th Annual International ACM-SIGIR Conference
+    on Research and Development in Information Retrieval.
+  - The proof for Equation 5 progresses from Equation 20 to Equation 22 by applying
+    the transformation motivated by Xie et al. [2021] and introducing the term p(R,
+    x1:i−1|z) to the equation.
+model-index:
+- name: SentenceTransformer based on BAAI/bge-small-en
+  results:
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: Unknown
+      type: unknown
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.01782178217821782
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.04356435643564356
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.06534653465346535
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.12475247524752475
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.01782178217821782
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.015841584158415842
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.016039603960396043
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.015841584158415842
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 1.839902956558168e-05
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 4.498766525563503e-05
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 7.262670252004521e-05
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.00015079859335392304
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.016300874257683427
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.04234598459845988
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.0018766020656866668
+      name: Cosine Map@100
+    - type: dot_accuracy@1
+      value: 0.01782178217821782
+      name: Dot Accuracy@1
+    - type: dot_accuracy@3
+      value: 0.04356435643564356
+      name: Dot Accuracy@3
+    - type: dot_accuracy@5
+      value: 0.06534653465346535
+      name: Dot Accuracy@5
+    - type: dot_accuracy@10
+      value: 0.12475247524752475
+      name: Dot Accuracy@10
+    - type: dot_precision@1
+      value: 0.01782178217821782
+      name: Dot Precision@1
+    - type: dot_precision@3
+      value: 0.015841584158415842
+      name: Dot Precision@3
+    - type: dot_precision@5
+      value: 0.016039603960396043
+      name: Dot Precision@5
+    - type: dot_precision@10
+      value: 0.015841584158415842
+      name: Dot Precision@10
+    - type: dot_recall@1
+      value: 1.839902956558168e-05
+      name: Dot Recall@1
+    - type: dot_recall@3
+      value: 4.498766525563503e-05
+      name: Dot Recall@3
+    - type: dot_recall@5
+      value: 7.262670252004521e-05
+      name: Dot Recall@5
+    - type: dot_recall@10
+      value: 0.00015079859335392304
+      name: Dot Recall@10
+    - type: dot_ndcg@10
+      value: 0.016300874257683427
+      name: Dot Ndcg@10
+    - type: dot_mrr@10
+      value: 0.04234598459845988
+      name: Dot Mrr@10
+    - type: dot_map@100
+      value: 0.0018766020656866668
+      name: Dot Map@100
+    - type: cosine_accuracy@1
+      value: 0.019801980198019802
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.040594059405940595
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.06534653465346535
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.12673267326732673
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.019801980198019802
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.01485148514851485
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.014851485148514853
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.016831683168316833
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 1.9670857914229207e-05
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 3.554268094376118e-05
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 6.67664165823309e-05
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.0001670844654494185
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.01679069935920913
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.04252396668238257
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.002057887757857092
+      name: Cosine Map@100
+    - type: dot_accuracy@1
+      value: 0.019801980198019802
+      name: Dot Accuracy@1
+    - type: dot_accuracy@3
+      value: 0.040594059405940595
+      name: Dot Accuracy@3
+    - type: dot_accuracy@5
+      value: 0.06534653465346535
+      name: Dot Accuracy@5
+    - type: dot_accuracy@10
+      value: 0.12673267326732673
+      name: Dot Accuracy@10
+    - type: dot_precision@1
+      value: 0.019801980198019802
+      name: Dot Precision@1
+    - type: dot_precision@3
+      value: 0.01485148514851485
+      name: Dot Precision@3
+    - type: dot_precision@5
+      value: 0.014851485148514853
+      name: Dot Precision@5
+    - type: dot_precision@10
+      value: 0.016831683168316833
+      name: Dot Precision@10
+    - type: dot_recall@1
+      value: 1.9670857914229207e-05
+      name: Dot Recall@1
+    - type: dot_recall@3
+      value: 3.554268094376118e-05
+      name: Dot Recall@3
+    - type: dot_recall@5
+      value: 6.67664165823309e-05
+      name: Dot Recall@5
+    - type: dot_recall@10
+      value: 0.0001670844654494185
+      name: Dot Recall@10
+    - type: dot_ndcg@10
+      value: 0.01679069935920913
+      name: Dot Ndcg@10
+    - type: dot_mrr@10
+      value: 0.04252396668238257
+      name: Dot Mrr@10
+    - type: dot_map@100
+      value: 0.002057887757857092
+      name: Dot Map@100
+    - type: cosine_accuracy@1
+      value: 0.01881188118811881
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.03762376237623762
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.06435643564356436
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.1306930693069307
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.01881188118811881
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.013861386138613862
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.015841584158415842
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.01722772277227723
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 1.8836739119030395e-05
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 3.852282962664283e-05
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 7.907232140954174e-05
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.00018073758516299118
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.01704492626324548
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.04188786735816444
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.002251865468050825
+      name: Cosine Map@100
+    - type: dot_accuracy@1
+      value: 0.01881188118811881
+      name: Dot Accuracy@1
+    - type: dot_accuracy@3
+      value: 0.03762376237623762
+      name: Dot Accuracy@3
+    - type: dot_accuracy@5
+      value: 0.06435643564356436
+      name: Dot Accuracy@5
+    - type: dot_accuracy@10
+      value: 0.1306930693069307
+      name: Dot Accuracy@10
+    - type: dot_precision@1
+      value: 0.01881188118811881
+      name: Dot Precision@1
+    - type: dot_precision@3
+      value: 0.013861386138613862
+      name: Dot Precision@3
+    - type: dot_precision@5
+      value: 0.015841584158415842
+      name: Dot Precision@5
+    - type: dot_precision@10
+      value: 0.01722772277227723
+      name: Dot Precision@10
+    - type: dot_recall@1
+      value: 1.8836739119030395e-05
+      name: Dot Recall@1
+    - type: dot_recall@3
+      value: 3.852282962664283e-05
+      name: Dot Recall@3
+    - type: dot_recall@5
+      value: 7.907232140954174e-05
+      name: Dot Recall@5
+    - type: dot_recall@10
+      value: 0.00018073758516299118
+      name: Dot Recall@10
+    - type: dot_ndcg@10
+      value: 0.01704492626324548
+      name: Dot Ndcg@10
+    - type: dot_mrr@10
+      value: 0.04188786735816444
+      name: Dot Mrr@10
+    - type: dot_map@100
+      value: 0.002251865468050825
+      name: Dot Map@100
+    - type: cosine_accuracy@1
+      value: 0.01881188118811881
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.03663366336633663
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.06435643564356436
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.1306930693069307
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.01881188118811881
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.013531353135313529
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.015643564356435644
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.01722772277227723
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 1.8836739119030395e-05
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 3.715905688573237e-05
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 7.929088142504806e-05
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.0001757722267344924
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.01701867523723249
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.0418477919220494
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.0022453604762727357
+      name: Cosine Map@100
+    - type: dot_accuracy@1
+      value: 0.01881188118811881
+      name: Dot Accuracy@1
+    - type: dot_accuracy@3
+      value: 0.03663366336633663
+      name: Dot Accuracy@3
+    - type: dot_accuracy@5
+      value: 0.06435643564356436
+      name: Dot Accuracy@5
+    - type: dot_accuracy@10
+      value: 0.1306930693069307
+      name: Dot Accuracy@10
+    - type: dot_precision@1
+      value: 0.01881188118811881
+      name: Dot Precision@1
+    - type: dot_precision@3
+      value: 0.013531353135313529
+      name: Dot Precision@3
+    - type: dot_precision@5
+      value: 0.015643564356435644
+      name: Dot Precision@5
+    - type: dot_precision@10
+      value: 0.01722772277227723
+      name: Dot Precision@10
+    - type: dot_recall@1
+      value: 1.8836739119030395e-05
+      name: Dot Recall@1
+    - type: dot_recall@3
+      value: 3.715905688573237e-05
+      name: Dot Recall@3
+    - type: dot_recall@5
+      value: 7.929088142504806e-05
+      name: Dot Recall@5
+    - type: dot_recall@10
+      value: 0.0001757722267344924
+      name: Dot Recall@10
+    - type: dot_ndcg@10
+      value: 0.01701867523723249
+      name: Dot Ndcg@10
+    - type: dot_mrr@10
+      value: 0.0418477919220494
+      name: Dot Mrr@10
+    - type: dot_map@100
+      value: 0.0022453604762727357
+      name: Dot Map@100
+---
+
+# SentenceTransformer based on BAAI/bge-small-en
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-small-en](https://huggingface.co/BAAI/bge-small-en). It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
+
+## Model Details
+
+### Model Description
+- **Model Type:** Sentence Transformer
+- **Base model:** [BAAI/bge-small-en](https://huggingface.co/BAAI/bge-small-en) <!-- at revision 2275a7bdee235e9b4f01fa73aa60d3311983cfea -->
+- **Maximum Sequence Length:** 512 tokens
+- **Output Dimensionality:** 384 tokens
+- **Similarity Function:** Cosine Similarity
+<!-- - **Training Dataset:** Unknown -->
+<!-- - **Language:** Unknown -->
+<!-- - **License:** Unknown -->
+
+### Model Sources
+
+- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
+- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
+- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
+
+### Full Model Architecture
+
+```
+SentenceTransformer(
+  (0): Transformer({'max_seq_length': 512, 'do_lower_case': True}) with Transformer model: BertModel 
+  (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+  (2): Normalize()
+)
+```
+
+## Usage
+
+### Direct Usage (Sentence Transformers)
+
+First install the Sentence Transformers library:
+
+```bash
+pip install -U sentence-transformers
+```
+
+Then you can load this model and run inference.
+```python
+from sentence_transformers import SentenceTransformer
+
+# Download from the 🤗 Hub
+model = SentenceTransformer("Areeb-02/bge-small-en-MultiplrRankingLoss-30-Rag-paper-dataset")
+# Run inference
+sentences = [
+    'Compare the top-5 retrieval accuracy of BM25 + MQ and SERM + BF for the NQ Dataset and HotpotQA.',
+    "For the NQ Dataset, SERM + BF has a top-5 retrieval accuracy of 88.22, which is significantly higher than BM25 + MQ's accuracy of 25.19. For HotpotQA, SERM + BF was not tested, but BM25 + MQ has a top-5 retrieval accuracy of 49.52.",
+    'The proof for Equation 5 progresses from Equation 20 to Equation 22 by applying the transformation motivated by Xie et al. [2021] and introducing the term p(R, x1:i−1|z) to the equation.',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 384]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(embeddings, embeddings)
+print(similarities.shape)
+# [3, 3]
+```
+
+<!--
+### Direct Usage (Transformers)
+
+<details><summary>Click to see the direct usage in Transformers</summary>
+
+</details>
+-->
+
+<!--
+### Downstream Usage (Sentence Transformers)
+
+You can finetune this model on your own dataset.
+
+<details><summary>Click to expand</summary>
+
+</details>
+-->
+
+<!--
+### Out-of-Scope Use
+
+*List how the model may foreseeably be misused and address what users ought not to do with the model.*
+-->
+
+## Evaluation
+
+### Metrics
+
+#### Information Retrieval
+
+* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| cosine_accuracy@1   | 0.0178     |
+| cosine_accuracy@3   | 0.0436     |
+| cosine_accuracy@5   | 0.0653     |
+| cosine_accuracy@10  | 0.1248     |
+| cosine_precision@1  | 0.0178     |
+| cosine_precision@3  | 0.0158     |
+| cosine_precision@5  | 0.016      |
+| cosine_precision@10 | 0.0158     |
+| cosine_recall@1     | 0.0        |
+| cosine_recall@3     | 0.0        |
+| cosine_recall@5     | 0.0001     |
+| cosine_recall@10    | 0.0002     |
+| cosine_ndcg@10      | 0.0163     |
+| cosine_mrr@10       | 0.0423     |
+| **cosine_map@100**  | **0.0019** |
+| dot_accuracy@1      | 0.0178     |
+| dot_accuracy@3      | 0.0436     |
+| dot_accuracy@5      | 0.0653     |
+| dot_accuracy@10     | 0.1248     |
+| dot_precision@1     | 0.0178     |
+| dot_precision@3     | 0.0158     |
+| dot_precision@5     | 0.016      |
+| dot_precision@10    | 0.0158     |
+| dot_recall@1        | 0.0        |
+| dot_recall@3        | 0.0        |
+| dot_recall@5        | 0.0001     |
+| dot_recall@10       | 0.0002     |
+| dot_ndcg@10         | 0.0163     |
+| dot_mrr@10          | 0.0423     |
+| dot_map@100         | 0.0019     |
+
+#### Information Retrieval
+
+* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| cosine_accuracy@1   | 0.0198     |
+| cosine_accuracy@3   | 0.0406     |
+| cosine_accuracy@5   | 0.0653     |
+| cosine_accuracy@10  | 0.1267     |
+| cosine_precision@1  | 0.0198     |
+| cosine_precision@3  | 0.0149     |
+| cosine_precision@5  | 0.0149     |
+| cosine_precision@10 | 0.0168     |
+| cosine_recall@1     | 0.0        |
+| cosine_recall@3     | 0.0        |
+| cosine_recall@5     | 0.0001     |
+| cosine_recall@10    | 0.0002     |
+| cosine_ndcg@10      | 0.0168     |
+| cosine_mrr@10       | 0.0425     |
+| **cosine_map@100**  | **0.0021** |
+| dot_accuracy@1      | 0.0198     |
+| dot_accuracy@3      | 0.0406     |
+| dot_accuracy@5      | 0.0653     |
+| dot_accuracy@10     | 0.1267     |
+| dot_precision@1     | 0.0198     |
+| dot_precision@3     | 0.0149     |
+| dot_precision@5     | 0.0149     |
+| dot_precision@10    | 0.0168     |
+| dot_recall@1        | 0.0        |
+| dot_recall@3        | 0.0        |
+| dot_recall@5        | 0.0001     |
+| dot_recall@10       | 0.0002     |
+| dot_ndcg@10         | 0.0168     |
+| dot_mrr@10          | 0.0425     |
+| dot_map@100         | 0.0021     |
+
+#### Information Retrieval
+
+* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| cosine_accuracy@1   | 0.0188     |
+| cosine_accuracy@3   | 0.0376     |
+| cosine_accuracy@5   | 0.0644     |
+| cosine_accuracy@10  | 0.1307     |
+| cosine_precision@1  | 0.0188     |
+| cosine_precision@3  | 0.0139     |
+| cosine_precision@5  | 0.0158     |
+| cosine_precision@10 | 0.0172     |
+| cosine_recall@1     | 0.0        |
+| cosine_recall@3     | 0.0        |
+| cosine_recall@5     | 0.0001     |
+| cosine_recall@10    | 0.0002     |
+| cosine_ndcg@10      | 0.017      |
+| cosine_mrr@10       | 0.0419     |
+| **cosine_map@100**  | **0.0023** |
+| dot_accuracy@1      | 0.0188     |
+| dot_accuracy@3      | 0.0376     |
+| dot_accuracy@5      | 0.0644     |
+| dot_accuracy@10     | 0.1307     |
+| dot_precision@1     | 0.0188     |
+| dot_precision@3     | 0.0139     |
+| dot_precision@5     | 0.0158     |
+| dot_precision@10    | 0.0172     |
+| dot_recall@1        | 0.0        |
+| dot_recall@3        | 0.0        |
+| dot_recall@5        | 0.0001     |
+| dot_recall@10       | 0.0002     |
+| dot_ndcg@10         | 0.017      |
+| dot_mrr@10          | 0.0419     |
+| dot_map@100         | 0.0023     |
+
+#### Information Retrieval
+
+* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| cosine_accuracy@1   | 0.0188     |
+| cosine_accuracy@3   | 0.0366     |
+| cosine_accuracy@5   | 0.0644     |
+| cosine_accuracy@10  | 0.1307     |
+| cosine_precision@1  | 0.0188     |
+| cosine_precision@3  | 0.0135     |
+| cosine_precision@5  | 0.0156     |
+| cosine_precision@10 | 0.0172     |
+| cosine_recall@1     | 0.0        |
+| cosine_recall@3     | 0.0        |
+| cosine_recall@5     | 0.0001     |
+| cosine_recall@10    | 0.0002     |
+| cosine_ndcg@10      | 0.017      |
+| cosine_mrr@10       | 0.0418     |
+| **cosine_map@100**  | **0.0022** |
+| dot_accuracy@1      | 0.0188     |
+| dot_accuracy@3      | 0.0366     |
+| dot_accuracy@5      | 0.0644     |
+| dot_accuracy@10     | 0.1307     |
+| dot_precision@1     | 0.0188     |
+| dot_precision@3     | 0.0135     |
+| dot_precision@5     | 0.0156     |
+| dot_precision@10    | 0.0172     |
+| dot_recall@1        | 0.0        |
+| dot_recall@3        | 0.0        |
+| dot_recall@5        | 0.0001     |
+| dot_recall@10       | 0.0002     |
+| dot_ndcg@10         | 0.017      |
+| dot_mrr@10          | 0.0418     |
+| dot_map@100         | 0.0022     |
+
+<!--
+## Bias, Risks and Limitations
+
+*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
+-->
+
+<!--
+### Recommendations
+
+*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
+-->
+
+## Training Details
+
+### Training Dataset
+
+#### Unnamed Dataset
+
+
+* Size: 1,010 training samples
+* Columns: <code>anchor</code> and <code>positive</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | anchor                                                                            | positive                                                                           |
+  |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
+  | type    | string                                                                            | string                                                                             |
+  | details | <ul><li>min: 2 tokens</li><li>mean: 21.28 tokens</li><li>max: 59 tokens</li></ul> | <ul><li>min: 2 tokens</li><li>mean: 40.15 tokens</li><li>max: 129 tokens</li></ul> |
+* Samples:
+  | anchor                                                                                                                                                   | positive                                                                                                                                                                                                                                                                                                                                                                   |
+  |:---------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>What is the purpose of the MultiHop-RAG dataset and what does it consist of?</code>                                                                | <code>The MultiHop-RAG dataset is developed to benchmark Retrieval-Augmented Generation (RAG) for multi-hop queries. It consists of a knowledge base, a large collection of multi-hop queries, their ground-truth answers, and the associated supporting evidence. The dataset is built using an English news article dataset as the underlying RAG knowledge base.</code> |
+  | <code>Among Google, Apple, and Nvidia, which company reported the largest profit margins in their third-quarter reports for the fiscal year 2023?</code> | <code>Apple reported the largest profit margins in their third-quarter reports for the fiscal year 2023.</code>                                                                                                                                                                                                                                                            |
+  | <code>Under what circumstances should the LLM answer the questions?</code>                                                                               | <code>The LLM should answer the questions based solely on the information provided in the paragraphs, and it should not use any other information.</code>                                                                                                                                                                                                                  |
+* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "cos_sim"
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 16
+- `per_device_eval_batch_size`: 16
+- `num_train_epochs`: 10
+- `warmup_ratio`: 0.1
+- `fp16`: True
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: steps
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 16
+- `per_device_eval_batch_size`: 16
+- `per_gpu_train_batch_size`: None
+- `per_gpu_eval_batch_size`: None
+- `gradient_accumulation_steps`: 1
+- `eval_accumulation_steps`: None
+- `learning_rate`: 5e-05
+- `weight_decay`: 0.0
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `max_grad_norm`: 1.0
+- `num_train_epochs`: 10
+- `max_steps`: -1
+- `lr_scheduler_type`: linear
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.1
+- `warmup_steps`: 0
+- `log_level`: passive
+- `log_level_replica`: warning
+- `log_on_each_node`: True
+- `logging_nan_inf_filter`: True
+- `save_safetensors`: True
+- `save_on_each_node`: False
+- `save_only_model`: False
+- `restore_callback_states_from_checkpoint`: False
+- `no_cuda`: False
+- `use_cpu`: False
+- `use_mps_device`: False
+- `seed`: 42
+- `data_seed`: None
+- `jit_mode_eval`: False
+- `use_ipex`: False
+- `bf16`: False
+- `fp16`: True
+- `fp16_opt_level`: O1
+- `half_precision_backend`: auto
+- `bf16_full_eval`: False
+- `fp16_full_eval`: False
+- `tf32`: None
+- `local_rank`: 0
+- `ddp_backend`: None
+- `tpu_num_cores`: None
+- `tpu_metrics_debug`: False
+- `debug`: []
+- `dataloader_drop_last`: False
+- `dataloader_num_workers`: 0
+- `dataloader_prefetch_factor`: None
+- `past_index`: -1
+- `disable_tqdm`: False
+- `remove_unused_columns`: True
+- `label_names`: None
+- `load_best_model_at_end`: False
+- `ignore_data_skip`: False
+- `fsdp`: []
+- `fsdp_min_num_params`: 0
+- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
+- `fsdp_transformer_layer_cls_to_wrap`: None
+- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
+- `deepspeed`: None
+- `label_smoothing_factor`: 0.0
+- `optim`: adamw_torch
+- `optim_args`: None
+- `adafactor`: False
+- `group_by_length`: False
+- `length_column_name`: length
+- `ddp_find_unused_parameters`: None
+- `ddp_bucket_cap_mb`: None
+- `ddp_broadcast_buffers`: False
+- `dataloader_pin_memory`: True
+- `dataloader_persistent_workers`: False
+- `skip_memory_metrics`: True
+- `use_legacy_prediction_loop`: False
+- `push_to_hub`: False
+- `resume_from_checkpoint`: None
+- `hub_model_id`: None
+- `hub_strategy`: every_save
+- `hub_private_repo`: False
+- `hub_always_push`: False
+- `gradient_checkpointing`: False
+- `gradient_checkpointing_kwargs`: None
+- `include_inputs_for_metrics`: False
+- `eval_do_concat_batches`: True
+- `fp16_backend`: auto
+- `push_to_hub_model_id`: None
+- `push_to_hub_organization`: None
+- `mp_parameters`: 
+- `auto_find_batch_size`: False
+- `full_determinism`: False
+- `torchdynamo`: None
+- `ray_scope`: last
+- `ddp_timeout`: 1800
+- `torch_compile`: False
+- `torch_compile_backend`: None
+- `torch_compile_mode`: None
+- `dispatch_batches`: None
+- `split_batches`: None
+- `include_tokens_per_second`: False
+- `include_num_input_tokens_seen`: False
+- `neftune_noise_alpha`: None
+- `optim_target_modules`: None
+- `batch_eval_metrics`: False
+- `eval_on_start`: False
+- `batch_sampler`: batch_sampler
+- `multi_dataset_batch_sampler`: proportional
+
+</details>
+
+### Training Logs
+| Epoch  | Step | Training Loss | cosine_map@100 |
+|:------:|:----:|:-------------:|:--------------:|
+| 0      | 0    | -             | 0.0018         |
+| 1.5625 | 100  | -             | 0.0019         |
+| 3.0    | 192  | -             | 0.0020         |
+| 1.5625 | 100  | -             | 0.0021         |
+| 3.125  | 200  | -             | 0.0020         |
+| 4.6875 | 300  | -             | 0.0021         |
+| 5.0    | 320  | -             | 0.0020         |
+| 1.5625 | 100  | -             | 0.0020         |
+| 3.125  | 200  | -             | 0.0021         |
+| 4.6875 | 300  | -             | 0.0022         |
+| 1.5625 | 100  | -             | 0.0021         |
+| 3.125  | 200  | -             | 0.0019         |
+| 4.6875 | 300  | -             | 0.0022         |
+| 6.25   | 400  | -             | 0.0022         |
+| 7.8125 | 500  | 0.0021        | 0.0022         |
+| 9.375  | 600  | -             | 0.0023         |
+| 10.0   | 640  | -             | 0.0022         |
+
+
+### Framework Versions
+- Python: 3.10.12
+- Sentence Transformers: 3.0.1
+- Transformers: 4.42.3
+- PyTorch: 2.3.0+cu121
+- Accelerate: 0.32.1
+- Datasets: 2.20.0
+- Tokenizers: 0.19.1
+
+## Citation
+
+### BibTeX
+
+#### Sentence Transformers
+```bibtex
+@inproceedings{reimers-2019-sentence-bert,
+    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
+    author = "Reimers, Nils and Gurevych, Iryna",
+    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
+    month = "11",
+    year = "2019",
+    publisher = "Association for Computational Linguistics",
+    url = "https://arxiv.org/abs/1908.10084",
+}
+```
+
+#### MultipleNegativesRankingLoss
+```bibtex
+@misc{henderson2017efficient,
+    title={Efficient Natural Language Response Suggestion for Smart Reply}, 
+    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
+    year={2017},
+    eprint={1705.00652},
+    archivePrefix={arXiv},
+    primaryClass={cs.CL}
+}
+```
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

config.json

@@ -0,0 +1,31 @@
+{
+  "_name_or_path": "BAAI/bge-small-en",
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 384,
+  "id2label": {
+    "0": "LABEL_0"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 1536,
+  "label2id": {
+    "LABEL_0": 0
+  },
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.42.3",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.0.1",
+    "transformers": "4.42.3",
+    "pytorch": "2.3.0+cu121"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": null
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0461d946f0d099914e8a1b448d79b5d56d531e168a42abe8051915c7f2d986fd
+size 133462128

modules.json

@@ -0,0 +1,20 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  },
+  {
+    "idx": 2,
+    "name": "2",
+    "path": "2_Normalize",
+    "type": "sentence_transformers.models.Normalize"
+  }
+]

sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+  "max_seq_length": 512,
+  "do_lower_case": true
+}

special_tokens_map.json

@@ -0,0 +1,37 @@
+{
+  "cls_token": {
+    "content": "[CLS]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "[MASK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "[PAD]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "[SEP]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

@@ -0,0 +1,57 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "101": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "102": {
+      "content": "[SEP]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "103": {
+      "content": "[MASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "never_split": null,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}