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

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+---
+language:
+- en
+license: apache-2.0
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:35964
+- loss:MatryoshkaLoss
+- loss:MultipleNegativesRankingLoss
+base_model: nomic-ai/nomic-embed-text-v1.5
+widget:
+- source_sentence: Despite the crucial role of phosphorus in global food production,
+    there is a lack of comprehensive analysis on the economic and policy aspects of
+    phosphorus supply and demand, highlighting a significant knowledge gap in the
+    field of natural resource economics.
+  sentences:
+  - The human brain is intrinsically organized into dynamic, anticorrelated functional
+    networks
+  - 'The story of phosphorus: Global food security and food for thought'
+  - Identifying a knowledge gap in the field of study
+- source_sentence: Despite the comprehensive data sources used in this analysis, it
+    is important to note that uncertainties remain in the estimation of global precipitation,
+    particularly in data-sparse regions, and careful interpretation of the findings
+    is advised.
+  sentences:
+  - The shuttle radar topography mission—a new class of digital elevation models acquired
+    by spaceborne radar
+  - Advising cautious interpretation of the findings
+  - 'Global Precipitation: A 17-Year Monthly Analysis Based on Gauge Observations,
+    Satellite Estimates, and Numerical Model Outputs'
+- source_sentence: The study found that participants' value functions were characterized
+    by loss aversion, risk aversion, and the concavity of the utility function in
+    gains and the convexity in losses.
+  sentences:
+  - Ordered mesoporous molecular sieves synthesized by a liquid-crystal template mechanism
+  - 'Prospect theory: An analysis of decision under risk'
+  - Summarising the results section
+- source_sentence: Further research is needed to explore the potential role of individual
+    amino acids in optimizing protein intake and promoting optimal health outcomes.
+  sentences:
+  - Suggestions for future work
+  - Validation of a modified Early Warning Score in medical admissions
+  - Dietary Reference Intakes for Energy, Carbohydrate, Fiber, Fat, Fatty Acids, Cholesterol,
+    Protein and Amino Acids
+- source_sentence: The IANA Task Force (2021) builds upon previous research suggesting
+    that slower gait speed is associated with increased risk of adverse outcomes in
+    older adults (Levine et al., 2015; Schoenfeld et al., 2016).
+  sentences:
+  - 'Transdisciplinary research in sustainability science: practice, principles, and
+    challenges'
+  - Gait speed at usual pace as a predictor of adverse outcomes in community-dwelling
+    older people an International Academy on Nutrition and Aging (IANA) Task Force
+  - Referring to another writer’s idea(s) or position
+datasets:
+- Corran/SciTopicTriplets
+pipeline_tag: sentence-similarity
+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
+model-index:
+- name: nomic-ai/nomic-embed-text-v1.5
+  results:
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: SciGen Eval Set
+      type: SciGen-Eval-Set
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.19750889679715303
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.5547153024911032
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.81605871886121
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.9893238434163701
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.19750889679715303
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.1849051008303677
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.16321174377224199
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.098932384341637
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.19750889679715303
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.5547153024911032
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.81605871886121
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.9893238434163701
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.5663698287874538
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.43265442297915546
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.433292401944685
+      name: Cosine Map@100
+---
+
+# nomic-ai/nomic-embed-text-v1.5
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [nomic-ai/nomic-embed-text-v1.5](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5) on the [sci_topic_triplets](https://huggingface.co/datasets/Corran/SciTopicTriplets) 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:** [nomic-ai/nomic-embed-text-v1.5](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5) <!-- at revision ac6fcd72429d86ff25c17895e47a9bfcfc50c1b2 -->
+- **Maximum Sequence Length:** 8192 tokens
+- **Output Dimensionality:** 768 dimensions
+- **Similarity Function:** Cosine Similarity
+- **Training Dataset:**
+    - [sci_topic_triplets](https://huggingface.co/datasets/Corran/SciTopicTriplets)
+- **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': 8192, 'do_lower_case': False}) with Transformer model: NomicBertModel 
+  (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("Corran/SciTopicNomicEmbed")
+# Run inference
+sentences = [
+    'The IANA Task Force (2021) builds upon previous research suggesting that slower gait speed is associated with increased risk of adverse outcomes in older adults (Levine et al., 2015; Schoenfeld et al., 2016).',
+    'Gait speed at usual pace as a predictor of adverse outcomes in community-dwelling older people an International Academy on Nutrition and Aging (IANA) Task Force',
+    'Referring to another writer’s idea(s) or position',
+]
+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: `SciGen-Eval-Set`
+* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| cosine_accuracy@1   | 0.1975     |
+| cosine_accuracy@3   | 0.5547     |
+| cosine_accuracy@5   | 0.8161     |
+| cosine_accuracy@10  | 0.9893     |
+| cosine_precision@1  | 0.1975     |
+| cosine_precision@3  | 0.1849     |
+| cosine_precision@5  | 0.1632     |
+| cosine_precision@10 | 0.0989     |
+| cosine_recall@1     | 0.1975     |
+| cosine_recall@3     | 0.5547     |
+| cosine_recall@5     | 0.8161     |
+| cosine_recall@10    | 0.9893     |
+| **cosine_ndcg@10**  | **0.5664** |
+| cosine_mrr@10       | 0.4327     |
+| cosine_map@100      | 0.4333     |
+
+<!--
+## 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
+
+#### sci_topic_triplets
+
+* Dataset: [sci_topic_triplets](https://huggingface.co/datasets/Corran/SciTopicTriplets) at [8bf9936](https://huggingface.co/datasets/Corran/SciTopicTriplets/tree/8bf9936b3b007670b076d43959cdc261383ff88f)
+* Size: 35,964 training samples
+* Columns: <code>query</code>, <code>positive</code>, and <code>negative</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | query                                                                              | positive                                                                          | negative                                                                          |
+  |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
+  | type    | string                                                                             | string                                                                            | string                                                                            |
+  | details | <ul><li>min: 17 tokens</li><li>mean: 40.37 tokens</li><li>max: 93 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 18.75 tokens</li><li>max: 56 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 10.74 tokens</li><li>max: 23 tokens</li></ul> |
+* Samples:
+  | query                                                                                                                                                                                                                                                                                                              | positive                                                                                                                                                                                                             | negative                                                                                           |
+  |:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------|
+  | <code>This study provides comprehensive estimates of life expectancy, all-cause mortality, and cause-specific mortality for 249 causes of death and 195 countries and territories from 1980 to 2015, allowing for a detailed understanding of global health trends and patterns over the past four decades.</code> | <code>Global, regional, and national life expectancy, all-cause mortality, and cause-specific mortality for 249 causes of death, 1980–2015: a systematic analysis for the Global Burden of Disease Study 2015</code> | <code>Explaining the significance of the current study</code>                                      |
+  | <code>This paper explores the relationship between the expected value and the volatility of the nominal excess return on stocks using a econometric approach.</code>                                                                                                                                               | <code>On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks</code>                                                                                                    | <code>Stating the focus, aim, or argument of a short paper</code>                                  |
+  | <code>Despite the increasing attention given to the role of audit committees and board of directors in mitigating earnings management, several studies have reported inconclusive or even negative findings.</code>                                                                                                | <code>Audit committee, board of director characteristics, and earnings management</code>                                                                                                                             | <code>General reference to previous research or scholarship: highlighting negative outcomes</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,
+          384,
+          256,
+          128,
+          64
+      ],
+      "matryoshka_weights": [
+          1,
+          1,
+          1,
+          1,
+          1
+      ],
+      "n_dims_per_step": -1
+  }
+  ```
+
+### Evaluation Dataset
+
+#### sci_topic_triplets
+
+* Dataset: [sci_topic_triplets](https://huggingface.co/datasets/Corran/SciTopicTriplets) at [8bf9936](https://huggingface.co/datasets/Corran/SciTopicTriplets/tree/8bf9936b3b007670b076d43959cdc261383ff88f)
+* Size: 4,495 evaluation samples
+* Columns: <code>query</code>, <code>positive</code>, and <code>negative</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | query                                                                             | positive                                                                          | negative                                                                          |
+  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
+  | type    | string                                                                            | string                                                                            | string                                                                            |
+  | details | <ul><li>min: 18 tokens</li><li>mean: 40.1 tokens</li><li>max: 87 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 18.75 tokens</li><li>max: 58 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 10.74 tokens</li><li>max: 23 tokens</li></ul> |
+* Samples:
+  | query                                                                                                                                                                                                                                                                                                                | positive                                                                                                    | negative                                                   |
+  |:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------|
+  | <code>In this cluster-randomised controlled trial, the authors aimed to evaluate the effectiveness of introducing the Medical Emergency Team (MET) system in reducing response times and improving patient outcomes in emergency departments.</code>                                                                 | <code>Introduction of the medical emergency team (MET) system: a cluster-randomised controlled trial</code> | <code>Some ways of introducing quotations</code>           |
+  | <code>In the data collection phase of our study, we employed both surveys and interviews as research methods. Specifically, we administered surveys to 200 participants and conducted interviews with 10 key industry experts to gather proportional data on various aspects of management science practices.</code> | <code>Research Methodology: A Step-by-Step Guide for Beginners</code>                                       | <code>Surveys and interviews: Reporting proportions</code> |
+  | <code>Several density functional theory (DFT) based chemical reactivity indexes, such as the Fukui functions and the electrophilic and nucleophilic indices, are discussed in detail for their ability to predict chemical reactivity.</code>                                                                        | <code>Chemical reactivity indexes in density functional theory</code>                                       | <code>General comments on the relevant literature</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,
+          384,
+          256,
+          128,
+          64
+      ],
+      "matryoshka_weights": [
+          1,
+          1,
+          1,
+          1,
+          1
+      ],
+      "n_dims_per_step": -1
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 256
+- `per_device_eval_batch_size`: 256
+- `learning_rate`: 2e-05
+- `num_train_epochs`: 10
+- `warmup_ratio`: 0.1
+- `fp16`: True
+- `load_best_model_at_end`: 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`: 256
+- `per_device_eval_batch_size`: 256
+- `per_gpu_train_batch_size`: None
+- `per_gpu_eval_batch_size`: None
+- `gradient_accumulation_steps`: 1
+- `eval_accumulation_steps`: None
+- `torch_empty_cache_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`: 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`: 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
+- `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`: None
+- `hub_always_push`: False
+- `gradient_checkpointing`: False
+- `gradient_checkpointing_kwargs`: None
+- `include_inputs_for_metrics`: False
+- `include_for_metrics`: []
+- `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
+- `use_liger_kernel`: False
+- `eval_use_gather_object`: False
+- `average_tokens_across_devices`: False
+- `prompts`: None
+- `batch_sampler`: batch_sampler
+- `multi_dataset_batch_sampler`: proportional
+
+</details>
+
+### Training Logs
+| Epoch  | Step | Training Loss | Validation Loss | SciGen-Eval-Set_cosine_ndcg@10 |
+|:------:|:----:|:-------------:|:---------------:|:------------------------------:|
+| 0      | 0    | -             | -               | 0.5454                         |
+| 0.1418 | 20   | 4.4872        | 3.1379          | 0.5468                         |
+| 0.2837 | 40   | 2.241         | 1.7162          | 0.5497                         |
+| 0.4255 | 60   | 1.5937        | 1.4834          | 0.5524                         |
+| 0.5674 | 80   | 1.5356        | 1.3911          | 0.5541                         |
+| 0.7092 | 100  | 1.4106        | 1.3277          | 0.5549                         |
+| 0.8511 | 120  | 1.2612        | 1.2919          | 0.5561                         |
+| 0.9929 | 140  | 1.3147        | 1.2642          | 0.5572                         |
+| 1.1348 | 160  | 1.1527        | 1.2529          | 0.5582                         |
+| 1.2766 | 180  | 1.2103        | 1.2388          | 0.5593                         |
+| 1.4184 | 200  | 1.2407        | 1.2235          | 0.5598                         |
+| 1.5603 | 220  | 1.1356        | 1.2101          | 0.5607                         |
+| 1.7021 | 240  | 1.1644        | 1.1938          | 0.5605                         |
+| 1.8440 | 260  | 1.1927        | 1.1864          | 0.5612                         |
+| 1.9858 | 280  | 1.1909        | 1.1800          | 0.5613                         |
+| 2.1277 | 300  | 1.0549        | 1.1785          | 0.5620                         |
+| 2.2695 | 320  | 1.0745        | 1.1755          | 0.5630                         |
+| 2.4113 | 340  | 1.1485        | 1.1656          | 0.5637                         |
+| 2.5532 | 360  | 1.1159        | 1.1654          | 0.5637                         |
+| 2.6950 | 380  | 1.0686        | 1.1623          | 0.5640                         |
+| 2.8369 | 400  | 1.1436        | 1.1594          | 0.5632                         |
+| 2.9787 | 420  | 1.0899        | 1.1534          | 0.5644                         |
+| 3.1206 | 440  | 1.0756        | 1.1512          | 0.5647                         |
+| 3.2624 | 460  | 1.0203        | 1.1536          | 0.5645                         |
+| 3.4043 | 480  | 1.1073        | 1.1564          | 0.5650                         |
+| 3.5461 | 500  | 1.0423        | 1.1594          | 0.5651                         |
+| 3.6879 | 520  | 1.069         | 1.1514          | 0.5652                         |
+| 3.8298 | 540  | 1.0101        | 1.1538          | 0.5645                         |
+| 3.9716 | 560  | 1.0685        | 1.1647          | 0.5650                         |
+| 4.1135 | 580  | 1.0326        | 1.1618          | 0.5653                         |
+| 4.2553 | 600  | 1.0729        | 1.1587          | 0.5648                         |
+| 4.3972 | 620  | 1.0417        | 1.1515          | 0.5655                         |
+| 4.5390 | 640  | 1.0438        | 1.1528          | 0.5657                         |
+| 4.6809 | 660  | 1.025         | 1.1433          | 0.5660                         |
+| 4.8227 | 680  | 1.0526        | 1.1382          | 0.5662                         |
+| 4.9645 | 700  | 1.0485        | 1.1392          | 0.5663                         |
+| 5.1064 | 720  | 1.0348        | 1.1411          | 0.5665                         |
+| 5.2482 | 740  | 1.1001        | 1.1511          | 0.5663                         |
+| 5.3901 | 760  | 1.0926        | 1.1625          | 0.5662                         |
+| 5.5319 | 780  | 1.0885        | 1.1487          | 0.5662                         |
+| 5.6738 | 800  | 1.0942        | 1.1492          | 0.5665                         |
+| 5.8156 | 820  | 1.0457        | 1.1465          | 0.5666                         |
+| 5.9574 | 840  | 1.0479        | 1.1461          | 0.5664                         |
+
+
+### Framework Versions
+- Python: 3.11.11
+- Sentence Transformers: 3.3.1
+- Transformers: 4.47.1
+- PyTorch: 2.5.1+cu124
+- Accelerate: 1.2.1
+- Datasets: 3.2.0
+- Tokenizers: 0.21.0
+
+## 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 +1,58 @@
+{
+  "_name_or_path": "nomic-ai/nomic-embed-text-v1.5",
+  "activation_function": "swiglu",
+  "architectures": [
+    "NomicBertModel"
+  ],
+  "attn_pdrop": 0.0,
+  "auto_map": {
+    "AutoConfig": "nomic-ai/nomic-bert-2048--configuration_hf_nomic_bert.NomicBertConfig",
+    "AutoModel": "nomic-ai/nomic-bert-2048--modeling_hf_nomic_bert.NomicBertModel",
+    "AutoModelForMaskedLM": "nomic-ai/nomic-bert-2048--modeling_hf_nomic_bert.NomicBertForPreTraining"
+  },
+  "bos_token_id": null,
+  "causal": false,
+  "dense_seq_output": true,
+  "embd_pdrop": 0.0,
+  "eos_token_id": null,
+  "fused_bias_fc": true,
+  "fused_dropout_add_ln": true,
+  "initializer_range": 0.02,
+  "layer_norm_epsilon": 1e-12,
+  "max_trained_positions": 2048,
+  "mlp_fc1_bias": false,
+  "mlp_fc2_bias": false,
+  "model_type": "nomic_bert",
+  "n_embd": 768,
+  "n_head": 12,
+  "n_inner": 3072,
+  "n_layer": 12,
+  "n_positions": 8192,
+  "pad_vocab_size_multiple": 64,
+  "parallel_block": false,
+  "parallel_block_tied_norm": false,
+  "prenorm": false,
+  "qkv_proj_bias": false,
+  "reorder_and_upcast_attn": false,
+  "resid_pdrop": 0.0,
+  "rotary_emb_base": 1000,
+  "rotary_emb_fraction": 1.0,
+  "rotary_emb_interleaved": false,
+  "rotary_emb_scale_base": null,
+  "rotary_scaling_factor": null,
+  "scale_attn_by_inverse_layer_idx": false,
+  "scale_attn_weights": true,
+  "summary_activation": null,
+  "summary_first_dropout": 0.0,
+  "summary_proj_to_labels": true,
+  "summary_type": "cls_index",
+  "summary_use_proj": true,
+  "torch_dtype": "float32",
+  "transformers_version": "4.47.1",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "use_flash_attn": true,
+  "use_rms_norm": false,
+  "use_xentropy": true,
+  "vocab_size": 30528
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.3.1",
+    "transformers": "4.47.1",
+    "pytorch": "2.5.1+cu124"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7bdfd89a53ee34a3daf46e1b10e3814ae8ed9527b728ad2c6205c855d2214b68
+size 546938168

modules.json

@@ -0,0 +1,14 @@
+[
+  {
+    "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": 8192,
+  "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
+  },
+  "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 +1,56 @@
+{
+  "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_lower_case": true,
+  "extra_special_tokens": {},
+  "mask_token": "[MASK]",
+  "model_max_length": 8192,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}