Add new SentenceTransformer model.

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "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,
+  "pooling_mode_weightedmean_tokens": false,
+  "pooling_mode_lasttoken": false,
+  "include_prompt": true
+}

README.md

@@ -0,0 +1,895 @@
+---
+base_model: BAAI/bge-base-en-v1.5
+datasets:
+- bhlim/patentmatch_for_finetuning
+language:
+- en
+library_name: sentence-transformers
+license: apache-2.0
+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
+pipeline_tag: sentence-similarity
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:10136
+- loss:MatryoshkaLoss
+- loss:MultipleNegativesRankingLoss
+widget:
+- source_sentence: The UE sends the uplink signal including the identifier of the
+    uplink serving node to the downlink serving node and in this case the downlink
+    serving node learns the mapping relationship among the UE the uplink serving node
+    and the downlink serving node.The UE sends the uplink signal including the identifier
+    of the downlink serving node to the uplink serving node an in thiscase the uplink
+    serving node learns the mapping relationship among the UE the uplink serving node
+    and the downlink serving node.
+  sentences:
+  - A terminal for use in a wireless communication network comprising a plurality
+    of base stations the terminal arranged to communicate with the network via at
+    least two cells of a plurality of cells and to transmit a request for uplink resources
+    wherein the terminal is arranged to select at least one cell from among said plurality
+    of said cells for transmission of said request a resource for transmission of
+    said request from among a plurality of resources provided by a cell and a characteristic
+    of a signal used to transmit said request and to perform the selection in dependence
+    on at least one of the reason for said request the characteristics of an uplink
+    channel for transmission of said request and the preference of the network.
+  - The electronic device of any of claims 15 wherein the processor is further configured
+    to check whether the specific audio data is stored at the memory in response to
+    a play request on the specific audio data.
+  - The system of claim 1 or claim 2 comprising a plurality of said radiation emitting
+    devices.
+- source_sentence: Further in the example of Fig.35 the sound adjusting circuit 210
+    controls the sound outputs of the first to fourth speakers 161 to 164 based on
+    the sound data from the first to fifth detection sensors 420 to 428 so that the
+    first sound corresponding to the second display image is localized in the first
+    area 810 where the occupant of the driver seat 13 and the occupant of the rear
+    seat 18 equipped with the headrest 25 are located.Likewise the sound adjusting
+    circuit 210 controls the sound outputs of the first to fourth speakers 161 to
+    164 based on the sound data from the first to fifth detection sensors 420 to 428
+    so that the second sound corresponding to the first display image is localized
+    in the second area 820 where the occupant of the assistant drivers seat 12 and
+    the occupants of the rear seats 18 equipped with the headrests 26 and 27 respectively
+    are located.Accordingly the occupant of the rear seat 18 equipped with the headrest
+    26 who is located in the crosstalk area 603 in Fig.33 can now hear the second
+    sound clearly.
+  sentences:
+  - A gas turbine engine comprising a bladed rotor assembly 100200300400 according
+    to any one of Claims 1 to 9.
+  - The method of claim 1 further comprising sensing a distance between the display
+    and a user wherein applying the sound setting comprises applying the sound setting
+    based on the sensed distance between the display and the user and the obtained
+    curvature of the panel of the display.
+  - A developer carrying member that is capable of carrying a developer on a surface
+    thereof and that supplies the developer carried on the surface to a surface of
+    an image bearing member when a voltage is applied thereto comprising an elastic
+    layer and a surface layer that covers the elastic layer contains alumina and has
+    a higher volume resistivity than the elastic layer.
+- source_sentence: In the example of fig.1 a user 107 who arrives in the underground
+    area 109 and who has not yet subscribed to the electronic ticket service may subscribe
+    to the service by connecting his Bluetooth device 107a to a Bluetooth access point
+    104 of the service provider via a Bluetooth service device 104a.At the access
+    point 104 the customer 104 may perform a payment transaction select a desired
+    subscription and receive a link key.With the link key the users Bluetooth device
+    107a may subsequently establish secure Bluetooth connections with the Bluetooth
+    transceivers 101 and 102af.
+  sentences:
+  - A wireless communications device 102 for setting up a local service session in
+    a shortrange wireless communication network comprising means for sending 222 a
+    request for preconfiguration information over a longrange network 104 to a remote
+    destination 112 the preconfiguration information enabling establishment of the
+    local service session with a proximate wireless communications device 110means
+    for receiving 222 from the remote destination 112 the requested preconfiguration
+    information wherein the requested preconfiguration information includes one or
+    more security keys for performing an authentication process with the proximate
+    wireless communications device 110 over shortrange wireless communication means
+    for performing 220 an authentication process for establishing the local service
+    session with the proximate wireless communications device 110 over the shortrange
+    wireless communication using the received one or more security keys and means
+    for establishing 220 the local service session with the proximate wireless communications
+    device 112 over the shortrange wireless communications after the authentication
+    process.
+  - The mobile terminal any one of claims 2 to 4 wherein the controller 180 is further
+    configured to differently process a color of the image corresponding to the trajectory
+    of the second touch based on a position of the first touch.
+  - A detergent box assembly for a washing machine comprising a detergent box a distributor
+    box having a front plate a rear plate and a receiving chamber provided therebetween
+    said receiving chamber configured to store a laundry treat agent the distributor
+    box being movably disposed within the detergent box and adapted to move between
+    an open position and a closed position a keypress being provided in the front
+    plate and a driving subassembly disposed in at least one of the detergent box
+    and the distributor box and configured to drive the distributor box to move from
+    the closed position to the open position when the keypress is pressed.
+- source_sentence: The step of determining may comprisemeasuring a distance between
+    each surrogate server and each subnetwork according to the subnetwork of the user
+    selecting a surrogate server with the smallest distance.
+  sentences:
+  - The computer system of Claim 13 comprising a memory storing instructions which
+    when implemented on the one or more processors configure the computer system to
+    carry out the method of any one of Claims 1 to 10
+  - A cooking oven 1 comprising a housing 2 a cooking cavity 3 formed in the housing
+    2 and closable by a door 5 heating means 6 6 placed in thermal exchange relationship
+    with the cooking cavity 3 ventilating means placed in the housing 2 and having
+    one or more electrical fans 7 8 7 8 adapted to ventilate on one or more thermally
+    sensitive areas of the oven 1 a control system 10 connected to the heating means
+    6 6 and to the ventilating means and having a temperature detector 12 associated
+    with the cooking cavity 3 wherein the control system is configured to activate
+    and deactivate the heating means 6 6 depending on a temperature detected by the
+    temperature detector 12 characterized in that the control system 10 activates
+    and deactivates at least one of said one or more fans 7 8 7 8 automatically together
+    with the respective activation and deactivation of the heating means 6 6.
+  - The method of claim 12 wherein selecting the target control parameter further
+    comprises for the respective selected control parameters comparing the initial
+    turbine output with the predicted turbine output while operating the selected
+    control parameter with the adjustment of the selected control parameter to determine
+    an adjustment differential and selecting the target control parameter having the
+    target adjustment by using the adjustment differential of the target control parameter.
+- source_sentence: Referring to FIG.32 a a sink device 3200 is designed to display
+    thumbnail images in the metadata of contents received from source devices connected
+    via an integrated wire interface.As mentioned in the foregoing description if
+    a remote controller 3250 capable of outputting a pointing signal is situated within
+    a region of a specific thumbnail image 3260 side information e.g.Amanda 1st album
+    singer.Song etc.is displayed together.
+  sentences:
+  - The method of any one of claims 8 to 12 wherein the requesting for the broadcast
+    channel information comprises transmitting to the server image data obtained by
+    capturing the content being reproduced by the display apparatus or audio data
+    obtained by recording the content for a certain time.
+  - The electrode assembly of any one of the preceding claims wherein the first electrode
+    comprises a substrate 113 wherein the first active material layer comprises active
+    material layers 112 on both surfaces of the substrate and the ceramic layer comprises
+    ceramic material layers 50 on both surfaces of the substrate.
+  - A method according to claim 1 wherein said topsheet assembly is a threeply laminate
+    comprising an acquisition layer a nonwoven layer and a cuff assembly.
+model-index:
+- name: BGE base PatentMatch Matryoshka
+  results:
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 768
+      type: dim_768
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.042620363062352014
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.10142067876874507
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.14483030781373324
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.23204419889502761
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.042620363062352014
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.03380689292291502
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.02896606156274665
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.023204419889502764
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.042620363062352014
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.10142067876874507
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.14483030781373324
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.23204419889502761
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.12169609468606697
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.08838588842535165
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.10140867877546615
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 512
+      type: dim_512
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.04222573007103394
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.09352801894238358
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.14285714285714285
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.22454617205998423
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.04222573007103394
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.031176006314127862
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.028571428571428574
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.02245461720599842
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.04222573007103394
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.09352801894238358
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.14285714285714285
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.22454617205998423
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.11822400593872298
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.08611580912291245
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.09959411357742169
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 256
+      type: dim_256
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.04025256511444357
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.09155485398579322
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.13970007892659828
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.21981057616416733
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.04025256511444357
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.03051828466193107
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.02794001578531966
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.021981057616416732
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.04025256511444357
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.09155485398579322
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.13970007892659828
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.21981057616416733
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.11513294301691931
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.08350856917352567
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.09631638060202527
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 128
+      type: dim_128
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.037884767166535126
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.08602999210734018
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.13180741910023677
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.2079715864246251
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.037884767166535126
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.028676664035780054
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.02636148382004736
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.02079715864246251
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.037884767166535126
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.08602999210734018
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.13180741910023677
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.2079715864246251
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.10894233297304821
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.07907489883614581
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.09087791679720966
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 64
+      type: dim_64
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.032754538279400155
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.07419100236779795
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.11444356748224152
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.18468823993685873
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.032754538279400155
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.024730334122599312
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.022888713496448304
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.018468823993685875
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.032754538279400155
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.07419100236779795
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.11444356748224152
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.18468823993685873
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.0959638876946607
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.06921471166735564
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.08022788346205763
+      name: Cosine Map@100
+---
+
+# BGE base PatentMatch Matryoshka
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) on the [bhlim/patentmatch_for_finetuning](https://huggingface.co/datasets/bhlim/patentmatch_for_finetuning) dataset. It maps sentences & paragraphs to a 768-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-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) <!-- at revision a5beb1e3e68b9ab74eb54cfd186867f64f240e1a -->
+- **Maximum Sequence Length:** 512 tokens
+- **Output Dimensionality:** 768 tokens
+- **Similarity Function:** Cosine Similarity
+- **Training Dataset:**
+    - [bhlim/patentmatch_for_finetuning](https://huggingface.co/datasets/bhlim/patentmatch_for_finetuning)
+- **Language:** en
+- **License:** apache-2.0
+
+### 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': 768, '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("bhlim/bge-base-patentmatch")
+# Run inference
+sentences = [
+    'Referring to FIG.32 a a sink device 3200 is designed to display thumbnail images in the metadata of contents received from source devices connected via an integrated wire interface.As mentioned in the foregoing description if a remote controller 3250 capable of outputting a pointing signal is situated within a region of a specific thumbnail image 3260 side information e.g.Amanda 1st album singer.Song etc.is displayed together.',
+    'The method of any one of claims 8 to 12 wherein the requesting for the broadcast channel information comprises transmitting to the server image data obtained by capturing the content being reproduced by the display apparatus or audio data obtained by recording the content for a certain time.',
+    'The electrode assembly of any one of the preceding claims wherein the first electrode comprises a substrate 113 wherein the first active material layer comprises active material layers 112 on both surfaces of the substrate and the ceramic layer comprises ceramic material layers 50 on both surfaces of the substrate.',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 768]
+
+# 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
+* Dataset: `dim_768`
+* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| cosine_accuracy@1   | 0.0426     |
+| cosine_accuracy@3   | 0.1014     |
+| cosine_accuracy@5   | 0.1448     |
+| cosine_accuracy@10  | 0.232      |
+| cosine_precision@1  | 0.0426     |
+| cosine_precision@3  | 0.0338     |
+| cosine_precision@5  | 0.029      |
+| cosine_precision@10 | 0.0232     |
+| cosine_recall@1     | 0.0426     |
+| cosine_recall@3     | 0.1014     |
+| cosine_recall@5     | 0.1448     |
+| cosine_recall@10    | 0.232      |
+| cosine_ndcg@10      | 0.1217     |
+| cosine_mrr@10       | 0.0884     |
+| **cosine_map@100**  | **0.1014** |
+
+#### Information Retrieval
+* Dataset: `dim_512`
+* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| cosine_accuracy@1   | 0.0422     |
+| cosine_accuracy@3   | 0.0935     |
+| cosine_accuracy@5   | 0.1429     |
+| cosine_accuracy@10  | 0.2245     |
+| cosine_precision@1  | 0.0422     |
+| cosine_precision@3  | 0.0312     |
+| cosine_precision@5  | 0.0286     |
+| cosine_precision@10 | 0.0225     |
+| cosine_recall@1     | 0.0422     |
+| cosine_recall@3     | 0.0935     |
+| cosine_recall@5     | 0.1429     |
+| cosine_recall@10    | 0.2245     |
+| cosine_ndcg@10      | 0.1182     |
+| cosine_mrr@10       | 0.0861     |
+| **cosine_map@100**  | **0.0996** |
+
+#### Information Retrieval
+* Dataset: `dim_256`
+* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| cosine_accuracy@1   | 0.0403     |
+| cosine_accuracy@3   | 0.0916     |
+| cosine_accuracy@5   | 0.1397     |
+| cosine_accuracy@10  | 0.2198     |
+| cosine_precision@1  | 0.0403     |
+| cosine_precision@3  | 0.0305     |
+| cosine_precision@5  | 0.0279     |
+| cosine_precision@10 | 0.022      |
+| cosine_recall@1     | 0.0403     |
+| cosine_recall@3     | 0.0916     |
+| cosine_recall@5     | 0.1397     |
+| cosine_recall@10    | 0.2198     |
+| cosine_ndcg@10      | 0.1151     |
+| cosine_mrr@10       | 0.0835     |
+| **cosine_map@100**  | **0.0963** |
+
+#### Information Retrieval
+* Dataset: `dim_128`
+* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| cosine_accuracy@1   | 0.0379     |
+| cosine_accuracy@3   | 0.086      |
+| cosine_accuracy@5   | 0.1318     |
+| cosine_accuracy@10  | 0.208      |
+| cosine_precision@1  | 0.0379     |
+| cosine_precision@3  | 0.0287     |
+| cosine_precision@5  | 0.0264     |
+| cosine_precision@10 | 0.0208     |
+| cosine_recall@1     | 0.0379     |
+| cosine_recall@3     | 0.086      |
+| cosine_recall@5     | 0.1318     |
+| cosine_recall@10    | 0.208      |
+| cosine_ndcg@10      | 0.1089     |
+| cosine_mrr@10       | 0.0791     |
+| **cosine_map@100**  | **0.0909** |
+
+#### Information Retrieval
+* Dataset: `dim_64`
+* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| cosine_accuracy@1   | 0.0328     |
+| cosine_accuracy@3   | 0.0742     |
+| cosine_accuracy@5   | 0.1144     |
+| cosine_accuracy@10  | 0.1847     |
+| cosine_precision@1  | 0.0328     |
+| cosine_precision@3  | 0.0247     |
+| cosine_precision@5  | 0.0229     |
+| cosine_precision@10 | 0.0185     |
+| cosine_recall@1     | 0.0328     |
+| cosine_recall@3     | 0.0742     |
+| cosine_recall@5     | 0.1144     |
+| cosine_recall@10    | 0.1847     |
+| cosine_ndcg@10      | 0.096      |
+| cosine_mrr@10       | 0.0692     |
+| **cosine_map@100**  | **0.0802** |
+
+<!--
+## 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
+
+#### bhlim/patentmatch_for_finetuning
+
+* Dataset: [bhlim/patentmatch_for_finetuning](https://huggingface.co/datasets/bhlim/patentmatch_for_finetuning) at [8d60f21](https://huggingface.co/datasets/bhlim/patentmatch_for_finetuning/tree/8d60f211ba8eb3b64fcdd4615dd0d297cf713843)
+* Size: 10,136 training samples
+* Columns: <code>positive</code> and <code>anchor</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | positive                                                                            | anchor                                                                              |
+  |:--------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
+  | type    | string                                                                              | string                                                                              |
+  | details | <ul><li>min: 5 tokens</li><li>mean: 136.61 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 12 tokens</li><li>mean: 76.35 tokens</li><li>max: 512 tokens</li></ul> |
+* Samples:
+  | positive                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             | anchor                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       |
+  |:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>Furthermore according to this liquid consuming apparatus if the decompression level acting on the liquid sensing chamber 21 of the liquid container 1 i.e.the pressure loss arising in the connecting passage between the liquid storage portion 7 and the liquid sensing chamber 21 due to the flow rate outflowing from the liquid storage portion 7 because of distension of the diaphragm pump through application of the external force when external force is applied in the direction of expansion of volume of the diaphragm pump 42 asdepicted in FIG.6 has been set to a low level if sufficient liquid is present in the liquid container 1 the liquid sensing chamber 21 will experience substantially no change in volume.</code>                                                                                                                                                                                                                                                                                                                                                                                                                                 | <code>The liquid cartridge according to any of claims 4 to 5 further comprising a ground terminal 175c 176c 177c positioned in the second line.</code>                                                                                                                                                                                                                                                                                                                                       |
+  | <code>It is highly desirable for tires to have good wet skid resistance low rolling resistance and good wear characteristics.It has traditionally been very difficult to improve a tires wear characteristics without sacrificing its wet skid resistance and traction characteristics.These properties depend to a great extent on the dynamic viscoelastic properties of the rubbers utilized in making the tire.</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           | <code>The pneumatic tire of at least one of the previous claims wherein the rubber composition comprises from 5 to 20 phr of the oil and from 45 to 70 phr of the terpene phenol resin.</code>                                                                                                                                                                                                                                                                                               |
+  | <code>Before setting the environment of the mobile communication terminal a user stores a multimedia message composed of different kinds of contents i.e.images sounds and texts.For example reference block 201 indicates a multimedia message composed of several images sounds and texts.The user can select an image A a sound A and a text A for environment setting elements of the mobile communication terminal from the contents of the multimedia message and construct a theme like in block 203 using the selected image A sound A and text A.The MPU 101 maps the contents of the theme to environment setting elements of the mobile communication terminal i.e.a background screen a ringtone and a user name like in block 205.The MPU 101 then sets the environment of the mobile communication terminal using the mapped elements like in block 207 thereby automatically and collectively changing the environment of the mobile communication terminal.Mapping information about mapping between the selected contents of the multimediamessage and the environment setting elements of the mobile communication terminal is stored in the flash RAM 107.</code> | <code>A terminal for processing data comprising an output unit configured to output a chatting service window a receiving unit configured to receive a request for executing a chatting service and a first download request for downloading first data through the chatting service from a user and a controller configured to control to output the first data downloaded in response to the received first download request to a background screen of the chatting service window.</code> |
+* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
+  ```json
+  {
+      "loss": "MultipleNegativesRankingLoss",
+      "matryoshka_dims": [
+          768,
+          512,
+          256,
+          128,
+          64
+      ],
+      "matryoshka_weights": [
+          1,
+          1,
+          1,
+          1,
+          1
+      ],
+      "n_dims_per_step": -1
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: epoch
+- `per_device_train_batch_size`: 32
+- `per_device_eval_batch_size`: 16
+- `gradient_accumulation_steps`: 16
+- `learning_rate`: 2e-05
+- `num_train_epochs`: 4
+- `lr_scheduler_type`: cosine
+- `warmup_ratio`: 0.1
+- `bf16`: True
+- `tf32`: True
+- `load_best_model_at_end`: True
+- `optim`: adamw_torch_fused
+- `batch_sampler`: no_duplicates
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: epoch
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 32
+- `per_device_eval_batch_size`: 16
+- `per_gpu_train_batch_size`: None
+- `per_gpu_eval_batch_size`: None
+- `gradient_accumulation_steps`: 16
+- `eval_accumulation_steps`: None
+- `learning_rate`: 2e-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`: 4
+- `max_steps`: -1
+- `lr_scheduler_type`: cosine
+- `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`: True
+- `fp16`: False
+- `fp16_opt_level`: O1
+- `half_precision_backend`: auto
+- `bf16_full_eval`: False
+- `fp16_full_eval`: False
+- `tf32`: True
+- `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`: True
+- `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_fused
+- `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
+- `batch_sampler`: no_duplicates
+- `multi_dataset_batch_sampler`: proportional
+
+</details>
+
+### Training Logs
+| Epoch      | Step   | Training Loss | dim_128_cosine_map@100 | dim_256_cosine_map@100 | dim_512_cosine_map@100 | dim_64_cosine_map@100 | dim_768_cosine_map@100 |
+|:----------:|:------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:|
+| 0.5047     | 10     | 10.0459       | -                      | -                      | -                      | -                     | -                      |
+| 0.9590     | 19     | -             | 0.0849                 | 0.0915                 | 0.0939                 | 0.0778                | 0.0966                 |
+| 1.0095     | 20     | 7.1373        | -                      | -                      | -                      | -                     | -                      |
+| 1.5142     | 30     | 5.9969        | -                      | -                      | -                      | -                     | -                      |
+| 1.9685     | 39     | -             | 0.0890                 | 0.0965                 | 0.1007                 | 0.0795                | 0.1012                 |
+| 2.0189     | 40     | 5.2984        | -                      | -                      | -                      | -                     | -                      |
+| 2.5237     | 50     | 4.884         | -                      | -                      | -                      | -                     | -                      |
+| **2.9779** | **59** | **-**         | **0.091**              | **0.0967**             | **0.099**              | **0.0801**            | **0.1013**             |
+| 3.0284     | 60     | 4.6633        | -                      | -                      | -                      | -                     | -                      |
+| 3.5331     | 70     | 4.5226        | -                      | -                      | -                      | -                     | -                      |
+| 3.8360     | 76     | -             | 0.0909                 | 0.0963                 | 0.0996                 | 0.0802                | 0.1014                 |
+
+* The bold row denotes the saved checkpoint.
+
+### Framework Versions
+- Python: 3.10.12
+- Sentence Transformers: 3.0.1
+- Transformers: 4.41.2
+- PyTorch: 2.1.2+cu121
+- Accelerate: 0.32.1
+- Datasets: 2.19.1
+- 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",
+}
+```
+
+#### MatryoshkaLoss
+```bibtex
+@misc{kusupati2024matryoshka,
+    title={Matryoshka Representation Learning}, 
+    author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
+    year={2024},
+    eprint={2205.13147},
+    archivePrefix={arXiv},
+    primaryClass={cs.LG}
+}
+```
+
+#### 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 +1,32 @@
+{
+  "_name_or_path": "BAAI/bge-base-en-v1.5",
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "gradient_checkpointing": false,
+  "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": 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": "4.41.2",
+  "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": "4.41.2",
+    "pytorch": "2.1.2+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 sha256:6ad1b1d02b1d6371697e40440adf9472e5dda2109b8c1867a4cea7f4f15bbe54
+size 437951328

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]"
+}