Add new SentenceTransformer model.

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "word_embedding_dimension": 768,
+  "pooling_mode_cls_token": false,
+  "pooling_mode_mean_tokens": true,
+  "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 +1,803 @@
+---
+base_model: NeuML/pubmedbert-base-embeddings
+datasets: []
+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:530
+- loss:MatryoshkaLoss
+- loss:MultipleNegativesRankingLoss
+widget:
+- source_sentence: If you receive a BharatPe speaker that you didn't order, please
+    contact BharatPe support immediately. They will assist in resolving the issue
+    and advise on the next steps.
+  sentences:
+  - Can I control multiple BharatPe speakers from one app?
+  - What to do if the BharatPe speaker's transaction announcements are intermittently
+    silent?
+  - What should I do if I receive a BharatPe speaker without ordering it?
+- source_sentence: Remote control capabilities depend on the model of the BharatPe
+    speaker. Check if your model supports remote control through the BharatPe app
+    or a connected device.
+  sentences:
+  - How do I update my personal details in my Bharatpe account?
+  - What are the benefits of the BharatPe speaker?
+  - Can I control the BharatPe speaker remotely?
+- source_sentence: If the announcements are not clear, check the speaker's volume
+    settings and ensure it's not placed near noisy equipment. If clarity doesn't improve,
+    the speaker may need servicing.
+  sentences:
+  - What to do if my BharatPe speaker is not syncing with the transaction history
+    in the app?
+  - What should I do if the speaker is not announcing payments clearly?
+  - The speaker doesn't produce any sound, what can be done?
+- source_sentence: If the speaker is causing interference, try relocating it or other
+    devices to reduce the interference. Ensure there's a reasonable distance between
+    the speaker and other wireless equipment.
+  sentences:
+  - Can I use my Bharatpe device for international transactions?
+  - How do I know if my BharatPe speaker is under warranty?
+  - What should I do if the BharatPe speaker is causing interference with other wireless
+    devices?
+- source_sentence: I can understand and respond in multiple Indian regional languages.
+    Feel free to communicate with me in the language you're most comfortable with.
+  sentences:
+  - How can I check if the BharatPe speaker is receiving a network signal?
+  - Bharti, can you provide tips for effective online communication?
+  - Bharti, what languages can you understand and respond to?
+model-index:
+- name: pubmedbert-base-embedding Chatbot Matryoshka
+  results:
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 768
+      type: dim_768
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.7674418604651163
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.9069767441860465
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.9302325581395349
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.9302325581395349
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.7674418604651163
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.3023255813953489
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.18604651162790697
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.09302325581395349
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.7674418604651163
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.9069767441860465
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.9302325581395349
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.9302325581395349
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.8563596702043667
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.8313953488372093
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.8349894291754757
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 512
+      type: dim_512
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.6976744186046512
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.8837209302325582
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.9302325581395349
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.9302325581395349
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.6976744186046512
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.29457364341085274
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.18604651162790697
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.09302325581395349
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.6976744186046512
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.8837209302325582
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.9302325581395349
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.9302325581395349
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.8320432881662091
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.7984496124031009
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.8017447288993117
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 256
+      type: dim_256
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.7906976744186046
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.8837209302325582
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.9069767441860465
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.9069767441860465
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.7906976744186046
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.29457364341085274
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.1813953488372093
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.09069767441860466
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.7906976744186046
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.8837209302325582
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.9069767441860465
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.9069767441860465
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.8533147922143328
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.8352713178294573
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.8392285023210497
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 128
+      type: dim_128
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.6744186046511628
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.813953488372093
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.8837209302325582
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.9069767441860465
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.6744186046511628
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.2713178294573643
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.17674418604651165
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.09069767441860466
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.6744186046511628
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.813953488372093
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.8837209302325582
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.9069767441860465
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.794152105183587
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.7575858250276855
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.7600321150655651
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 64
+      type: dim_64
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.6046511627906976
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.7441860465116279
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.7906976744186046
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.8604651162790697
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.6046511627906976
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.24806201550387597
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.15813953488372093
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.08604651162790698
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.6046511627906976
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.7441860465116279
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.7906976744186046
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.8604651162790697
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.7220252449949186
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.6786083425618308
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.6823125300680127
+      name: Cosine Map@100
+---
+
+# pubmedbert-base-embedding Chatbot Matryoshka
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [NeuML/pubmedbert-base-embeddings](https://huggingface.co/NeuML/pubmedbert-base-embeddings). 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:** [NeuML/pubmedbert-base-embeddings](https://huggingface.co/NeuML/pubmedbert-base-embeddings) <!-- at revision ba210f40b1b6d555d675c2d1ed6372e44570fc3c -->
+- **Maximum Sequence Length:** 512 tokens
+- **Output Dimensionality:** 768 tokens
+- **Similarity Function:** Cosine Similarity
+<!-- - **Training Dataset:** Unknown -->
+- **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': False}) with Transformer model: BertModel 
+  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+)
+```
+
+## 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("MANMEET75/pubmedbert-base-embedding-Chatbot-Matryoshk")
+# Run inference
+sentences = [
+    "I can understand and respond in multiple Indian regional languages. Feel free to communicate with me in the language you're most comfortable with.",
+    'Bharti, what languages can you understand and respond to?',
+    'Bharti, can you provide tips for effective online communication?',
+]
+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   | 0.7674    |
+| cosine_accuracy@3   | 0.907     |
+| cosine_accuracy@5   | 0.9302    |
+| cosine_accuracy@10  | 0.9302    |
+| cosine_precision@1  | 0.7674    |
+| cosine_precision@3  | 0.3023    |
+| cosine_precision@5  | 0.186     |
+| cosine_precision@10 | 0.093     |
+| cosine_recall@1     | 0.7674    |
+| cosine_recall@3     | 0.907     |
+| cosine_recall@5     | 0.9302    |
+| cosine_recall@10    | 0.9302    |
+| cosine_ndcg@10      | 0.8564    |
+| cosine_mrr@10       | 0.8314    |
+| **cosine_map@100**  | **0.835** |
+
+#### 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   | 0.6977     |
+| cosine_accuracy@3   | 0.8837     |
+| cosine_accuracy@5   | 0.9302     |
+| cosine_accuracy@10  | 0.9302     |
+| cosine_precision@1  | 0.6977     |
+| cosine_precision@3  | 0.2946     |
+| cosine_precision@5  | 0.186      |
+| cosine_precision@10 | 0.093      |
+| cosine_recall@1     | 0.6977     |
+| cosine_recall@3     | 0.8837     |
+| cosine_recall@5     | 0.9302     |
+| cosine_recall@10    | 0.9302     |
+| cosine_ndcg@10      | 0.832      |
+| cosine_mrr@10       | 0.7984     |
+| **cosine_map@100**  | **0.8017** |
+
+#### 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   | 0.7907     |
+| cosine_accuracy@3   | 0.8837     |
+| cosine_accuracy@5   | 0.907      |
+| cosine_accuracy@10  | 0.907      |
+| cosine_precision@1  | 0.7907     |
+| cosine_precision@3  | 0.2946     |
+| cosine_precision@5  | 0.1814     |
+| cosine_precision@10 | 0.0907     |
+| cosine_recall@1     | 0.7907     |
+| cosine_recall@3     | 0.8837     |
+| cosine_recall@5     | 0.907      |
+| cosine_recall@10    | 0.907      |
+| cosine_ndcg@10      | 0.8533     |
+| cosine_mrr@10       | 0.8353     |
+| **cosine_map@100**  | **0.8392** |
+
+#### 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   | 0.6744   |
+| cosine_accuracy@3   | 0.814    |
+| cosine_accuracy@5   | 0.8837   |
+| cosine_accuracy@10  | 0.907    |
+| cosine_precision@1  | 0.6744   |
+| cosine_precision@3  | 0.2713   |
+| cosine_precision@5  | 0.1767   |
+| cosine_precision@10 | 0.0907   |
+| cosine_recall@1     | 0.6744   |
+| cosine_recall@3     | 0.814    |
+| cosine_recall@5     | 0.8837   |
+| cosine_recall@10    | 0.907    |
+| cosine_ndcg@10      | 0.7942   |
+| cosine_mrr@10       | 0.7576   |
+| **cosine_map@100**  | **0.76** |
+
+#### 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   | 0.6047     |
+| cosine_accuracy@3   | 0.7442     |
+| cosine_accuracy@5   | 0.7907     |
+| cosine_accuracy@10  | 0.8605     |
+| cosine_precision@1  | 0.6047     |
+| cosine_precision@3  | 0.2481     |
+| cosine_precision@5  | 0.1581     |
+| cosine_precision@10 | 0.086      |
+| cosine_recall@1     | 0.6047     |
+| cosine_recall@3     | 0.7442     |
+| cosine_recall@5     | 0.7907     |
+| cosine_recall@10    | 0.8605     |
+| cosine_ndcg@10      | 0.722      |
+| cosine_mrr@10       | 0.6786     |
+| **cosine_map@100**  | **0.6823** |
+
+<!--
+## 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: 530 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: 12 tokens</li><li>mean: 36.83 tokens</li><li>max: 107 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 18.54 tokens</li><li>max: 30 tokens</li></ul> |
+* Samples:
+  | positive                                                                                                                                                                                                                                                                                                                                                                            | anchor                                                                   |
+  |:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------|
+  | <code>BharatPe Speaker comes with the following benefits: - Helps you avoid payment fraud - Lightweight & Easy installation process - Compatible with SIM & GPRS connectivity - Comes with a battery, no hassle of constant charging - Available in 10 Languages - Cashback Offers - Free replacement To Know more and place an order, tap below http://bharatpe.in/speaker.</code> | <code>What are the benefits of the BharatPe speaker?</code>              |
+  | <code>BharatPe Speaker comes with the following benefits: - Helps you avoid payment fraud - Lightweight & Easy installation process - Compatible with SIM & GPRS connectivity - Comes with a battery, no hassle of constant charging - Available in 10 Languages - Cashback Offers - Free replacement To Know more and place an order, tap below http://bharatpe.in/speaker.</code> | <code>What advantages does the BharatPe speaker offer?</code>            |
+  | <code>BharatPe Speaker comes with the following benefits: - Helps you avoid payment fraud - Lightweight & Easy installation process - Compatible with SIM & GPRS connectivity - Comes with a battery, no hassle of constant charging - Available in 10 Languages - Cashback Offers - Free replacement To Know more and place an order, tap below http://bharatpe.in/speaker.</code> | <code>Can you outline the benefits of using the BharatPe speaker?</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`: 10
+- `lr_scheduler_type`: cosine
+- `warmup_ratio`: 0.1
+- `tf32`: False
+- `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`: 10
+- `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`: False
+- `fp16`: False
+- `fp16_opt_level`: O1
+- `half_precision_backend`: auto
+- `bf16_full_eval`: False
+- `fp16_full_eval`: False
+- `tf32`: False
+- `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 |
+|:----------:|:-----:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:|
+| 0.9412     | 1     | -             | 0.4829                 | 0.5338                 | 0.5921                 | 0.3235                | 0.6100                 |
+| 1.8824     | 2     | -             | 0.5767                 | 0.6175                 | 0.6588                 | 0.4176                | 0.6793                 |
+| 2.8235     | 3     | -             | 0.6337                 | 0.6776                 | 0.6979                 | 0.5083                | 0.7263                 |
+| 3.7647     | 4     | -             | 0.6588                 | 0.7257                 | 0.7297                 | 0.5840                | 0.7612                 |
+| 4.7059     | 5     | -             | 0.7049                 | 0.7766                 | 0.7643                 | 0.6151                | 0.7902                 |
+| 5.6471     | 6     | -             | 0.7374                 | 0.8257                 | 0.7890                 | 0.6519                | 0.7956                 |
+| 6.5882     | 7     | -             | 0.7573                 | 0.8261                 | 0.7912                 | 0.6689                | 0.7978                 |
+| 7.5294     | 8     | -             | 0.7590                 | 0.8275                 | 0.7958                 | 0.6811                | 0.8233                 |
+| **8.4706** | **9** | **-**         | **0.76**               | **0.8392**             | **0.7998**             | **0.6823**            | **0.8234**             |
+| 9.4118     | 10    | 4.944         | 0.7600                 | 0.8392                 | 0.8017                 | 0.6823                | 0.8350                 |
+
+* 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 +1,25 @@
+{
+  "_name_or_path": "NeuML/pubmedbert-base-embeddings",
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "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

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+oid sha256:dc564f9c865eac75fa610620772ae7b096b7a4f4b8e0937c1456227f12bc39a5
+size 437951328

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"
+  }
+]

sentence_bert_config.json

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

special_tokens_map.json

@@ -0,0 +1,37 @@
+{
+  "cls_token": {
+    "content": "[CLS]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
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+    "rstrip": false,
+    "single_word": false
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+    "normalized": false,
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+    "single_word": false
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+    "single_word": false
+  },
+  "unk_token": {
+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

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+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
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+      "lstrip": false,
+      "normalized": false,
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+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
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+  "never_split": null,
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+  "pad_token": "[PAD]",
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+  "padding_side": "right",
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+  "stride": 0,
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "[UNK]"
+}