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:35934
+- loss:MatryoshkaLoss
+- loss:MultipleNegativesRankingLoss
+base_model: nomic-ai/nomic-embed-text-v1.5
+widget:
+- source_sentence: Stating purpose of the current research with reference to gaps
+    or issues in the literature
+  sentences:
+  - During the 15-year study, 10% of the osseointegrated implants in the edentulous
+    jaw showed signs of peri-implantitis, leading to their failure.
+  - This paper provides an in-depth exploration of the qualitative case study methodology,
+    addressing the lack of comprehensive guidance for novice researchers in this area.
+  - As a novice researcher in management science, I have been drawn to the qualitative
+    case study methodology due to its ability to provide rich, in-depth insights into
+    complex real-world situations.
+- source_sentence: Indicating missing, weak, or contradictory evidence
+  sentences:
+  - This paper contributes to the literature on the financial system by examining
+    the relationship between bank size, bank capital, and the bank lending channel
+    using a unique dataset of US banks during the global financial crisis.
+  - A total of 150 patients with a clinical diagnosis of osteoarthritis of the hip
+    or knee, according to the American College of Rheumatology criteria, were included
+    in the study.
+  - Despite the widespread use of the WOMAC (Western Ontario and McMaster Universities
+    Osteoarthritis Index) questionnaire in clinical practice and research, there is
+    a lack of consensus regarding its responsiveness to antirheumatic drug therapy
+    in patients with osteoarthritis of the hip or knee.
+- source_sentence: 'Establishing the importance of the topic for the world or society:
+    time frame given'
+  sentences:
+  - The Th/Hf ratios of the basaltic lavas from the British Tertiary Volcanic Province
+    range from 4.2 to 5.5, as shown in Table 1.
+  - The use of organometal halide perovskites as visible-light sensitizers for photovoltaic
+    cells has gained significant attention in the optoelectronics community due to
+    their promising photovoltaic performance and cost-effective fabrication since
+    the late 2000s.
+  - Table 1 summarizes the power conversion efficiencies (PCEs) and certifications
+    of the best-performing perovskite solar cells reported in the literature.
+- source_sentence: Describing the research design and the methods used
+  sentences:
+  - This study aims to evaluate the efficacy and safety of preoperative radiotherapy
+    followed by total mesorectal excision in the treatment of resectable rectal cancer.
+  - TREE-PUZZLE's parallel computing implementation significantly reduces the time
+    required for maximum likelihood phylogenetic analysis compared to traditional
+    methods, supporting previous findings of the importance of parallelization in
+    phylogenetics.
+  - This study investigates the efficacy of preoperative radiotherapy followed by
+    total mesorectal excision in the treatment of resectable rectal cancer.
+- source_sentence: 'Surveys and interviews: Introducing excerpts from interview data'
+  sentences:
+  - Previous research on international trade under the WTO regime has explored various
+    approaches to understanding the uneven promotion of trade (Hoekstra & Kostecki,
+    2001; Cline, 2004, ...).
+  - Through surveys and interviews, multiliterate teachers expressed a shared belief
+    in the importance of fostering students' ability to navigate multiple discourse
+    communities.
+  - The authors employ a constructivist approach to learning, where students build
+    knowledge through active engagement with multimedia texts and collaborative discussions.
+datasets:
+- Corran/SciGenTriplets
+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: sentence-transformers/static-retrieval-mrl-en-v1
+  results:
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: SciGen Eval Set
+      type: SciGen-Eval-Set
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.9000445235975066
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.9452359750667854
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.9641585040071238
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.9853072128227961
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.9000445235975066
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.3150786583555951
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.19283170080142473
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.0985307212822796
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.9000445235975066
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.9452359750667854
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.9641585040071238
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.9853072128227961
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.941495085912059
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.9276217685055616
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.9283906979180744
+      name: Cosine Map@100
+---
+
+# sentence-transformers/static-retrieval-mrl-en-v1
+
+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_gen_colbert_triplets](https://huggingface.co/datasets/Corran/SciGenColbertTriplets) 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_gen_colbert_triplets](https://huggingface.co/datasets/Corran/SciGenColbertTriplets)
+- **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/SciGenNomicEmbed")
+# Run inference
+sentences = [
+    'Surveys and interviews: Introducing excerpts from interview data',
+    "Through surveys and interviews, multiliterate teachers expressed a shared belief in the importance of fostering students' ability to navigate multiple discourse communities.",
+    'The authors employ a constructivist approach to learning, where students build knowledge through active engagement with multimedia texts and collaborative discussions.',
+]
+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   | 0.9        |
+| cosine_accuracy@3   | 0.9452     |
+| cosine_accuracy@5   | 0.9642     |
+| cosine_accuracy@10  | 0.9853     |
+| cosine_precision@1  | 0.9        |
+| cosine_precision@3  | 0.3151     |
+| cosine_precision@5  | 0.1928     |
+| cosine_precision@10 | 0.0985     |
+| cosine_recall@1     | 0.9        |
+| cosine_recall@3     | 0.9452     |
+| cosine_recall@5     | 0.9642     |
+| cosine_recall@10    | 0.9853     |
+| **cosine_ndcg@10**  | **0.9415** |
+| cosine_mrr@10       | 0.9276     |
+| cosine_map@100      | 0.9284     |
+
+<!--
+## 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_gen_colbert_triplets
+
+* Dataset: [sci_gen_colbert_triplets](https://huggingface.co/datasets/Corran/SciGenColbertTriplets) at [44071bd](https://huggingface.co/datasets/Corran/SciGenColbertTriplets/tree/44071bdd857e9598233bd44a26a9433b46f25458)
+* Size: 35,934 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: 5 tokens</li><li>mean: 10.24 tokens</li><li>max: 23 tokens</li></ul> | <ul><li>min: 2 tokens</li><li>mean: 39.86 tokens</li><li>max: 80 tokens</li></ul> | <ul><li>min: 18 tokens</li><li>mean: 40.41 tokens</li><li>max: 88 tokens</li></ul> |
+* Samples:
+  | query                                                            | positive                                                                                                                                                                                                                                      | negative                                                                                                                                                                                                                                                                                |
+  |:-----------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>Previous research: highlighting negative outcomes</code>   | <code>Despite the widespread use of seniority-based wage systems in labor contracts, previous research has highlighted their negative outcomes, such as inefficiencies and demotivating effects on workers.</code>                            | <code>This paper, published in 1974, was among the first to establish the importance of rank-order tournaments as optimal labor contracts in microeconomics.</code>                                                                                                                     |
+  | <code>Synthesising sources: contrasting evidence or ideas</code> | <code>Despite the observed chronic enterocolitis in Interleukin-10-deficient mice, some studies suggest that this cytokine plays a protective role in intestinal inflammation in humans (Kurimoto et al., 2001).</code>                       | <code>Chronic enterocolitis developed in Interleukin-10-deficient mice, characterized by inflammatory cell infiltration, epithelial damage, and increased production of pro-inflammatory cytokines.</code>                                                                              |
+  | <code>Previous research: Approaches taken</code>                 | <code>Previous research on measuring patient-relevant outcomes in osteoarthritis has primarily relied on self-reported measures, such as the Western Ontario and McMaster Universities Arthritis Index (WOMAC) (Bellamy et al., 1988).</code> | <code>The WOMAC (Western Ontario and McMaster Universities Osteoarthritis Index) questionnaire has been widely used in physical therapy research to assess the impact of antirheumatic drug therapy on patient-reported outcomes in individuals with hip or knee osteoarthritis.</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_gen_colbert_triplets
+
+* Dataset: [sci_gen_colbert_triplets](https://huggingface.co/datasets/Corran/SciGenColbertTriplets) at [44071bd](https://huggingface.co/datasets/Corran/SciGenColbertTriplets/tree/44071bdd857e9598233bd44a26a9433b46f25458)
+* Size: 4,492 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: 5 tokens</li><li>mean: 10.23 tokens</li><li>max: 23 tokens</li></ul> | <ul><li>min: 18 tokens</li><li>mean: 39.83 tokens</li><li>max: 84 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 39.89 tokens</li><li>max: 84 tokens</li></ul> |
+* Samples:
+  | query                                                                                | positive                                                                                                                                                                                                               | negative                                                                                                                                                                                                                                                                                                                    |
+  |:-------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>Providing background information: reference to the purpose of the study</code> | <code>This study aimed to investigate the impact of socioeconomic status on child development, specifically focusing on cognitive, language, and social-emotional domains.</code>                                      | <code>Children from high socioeconomic status families showed significantly higher IQ scores (M = 112.5, SD = 5.6) compared to children from low socioeconomic status families (M = 104.3, SD = 6.2) in the verbal IQ subtest.</code>                                                                                       |
+  | <code>Providing background information: reference to the literature</code>           | <code>According to previous studies using WinGX suite for small-molecule single-crystal crystallography, the optimization of crystal structures leads to improved accuracy in determining atomic coordinates.</code>   | <code>This paper describes the WinGX suite, a powerful tool for small-molecule single-crystal crystallography that significantly advances the field of crystallography by streamlining data collection and analysis.</code>                                                                                                 |
+  | <code>General comments on the relevant literature</code>                             | <code>Polymer brushes have gained significant attention in the field of polymer science due to their unique properties, such as controlled thickness, high surface density, and tunable interfacial properties.</code> | <code>Despite previous reports suggesting that polymer brushes with short grafting densities exhibit poorer performance in terms of adhesion and stability compared to those with higher grafting densities (Liu et al., 2010), our results indicate that the opposite is true for certain types of polymer brushes.</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    | -             | -               | 0.1744                         |
+| 0.1418 | 20   | 31.1056       | 29.9614         | 0.2010                         |
+| 0.2837 | 40   | 28.3636       | 25.9021         | 0.3552                         |
+| 0.4255 | 60   | 23.8421       | 21.4941         | 0.4817                         |
+| 0.5674 | 80   | 20.2484       | 19.1669         | 0.5793                         |
+| 0.7092 | 100  | 18.6804       | 18.0565         | 0.6219                         |
+| 0.8511 | 120  | 17.7705       | 17.3231         | 0.6564                         |
+| 0.9929 | 140  | 17.1951       | 16.8645         | 0.6723                         |
+| 1.1348 | 160  | 16.1046       | 16.3714         | 0.6918                         |
+| 1.2766 | 180  | 16.0491       | 16.0427         | 0.7063                         |
+| 1.4184 | 200  | 15.4859       | 15.6624         | 0.7240                         |
+| 1.5603 | 220  | 15.3239       | 15.4609         | 0.7341                         |
+| 1.7021 | 240  | 14.9202       | 15.1556         | 0.7414                         |
+| 1.8440 | 260  | 14.7176       | 14.8438         | 0.7584                         |
+| 1.9858 | 280  | 14.5036       | 14.5248         | 0.7718                         |
+| 2.1277 | 300  | 12.8219       | 14.4285         | 0.7860                         |
+| 2.2695 | 320  | 12.9107       | 14.1397         | 0.7927                         |
+| 2.4113 | 340  | 12.6728       | 13.8471         | 0.8092                         |
+| 2.5532 | 360  | 12.4097       | 13.6623         | 0.8160                         |
+| 2.6950 | 380  | 12.3039       | 13.4078         | 0.8264                         |
+| 2.8369 | 400  | 12.121        | 13.1426         | 0.8382                         |
+| 2.9787 | 420  | 12.0307       | 12.7989         | 0.8520                         |
+| 3.1206 | 440  | 10.4306       | 12.7893         | 0.8566                         |
+| 3.2624 | 460  | 10.5238       | 12.7036         | 0.8681                         |
+| 3.4043 | 480  | 10.3648       | 12.5674         | 0.8783                         |
+| 3.5461 | 500  | 10.4774       | 12.3069         | 0.8794                         |
+| 3.6879 | 520  | 10.4965       | 12.0965         | 0.8837                         |
+| 3.8298 | 540  | 10.4085       | 12.0368         | 0.8868                         |
+| 3.9716 | 560  | 10.2881       | 11.9063         | 0.8946                         |
+| 4.1135 | 580  | 9.1967        | 11.9930         | 0.8970                         |
+| 4.2553 | 600  | 9.3798        | 11.8936         | 0.9047                         |
+| 4.3972 | 620  | 9.3375        | 11.7678         | 0.9118                         |
+| 4.5390 | 640  | 9.2483        | 11.7572         | 0.9078                         |
+| 4.6809 | 660  | 9.3736        | 11.6011         | 0.9174                         |
+| 4.8227 | 680  | 9.3427        | 11.5383         | 0.9197                         |
+| 4.9645 | 700  | 9.3935        | 11.4293         | 0.9242                         |
+| 5.1064 | 720  | 8.5631        | 11.5119         | 0.9294                         |
+| 5.2482 | 740  | 8.6057        | 11.5173         | 0.9255                         |
+| 5.3901 | 760  | 8.6059        | 11.5421         | 0.9263                         |
+| 5.5319 | 780  | 8.8488        | 11.3879         | 0.9304                         |
+| 5.6738 | 800  | 8.7855        | 11.3523         | 0.9320                         |
+| 5.8156 | 820  | 8.7525        | 11.2572         | 0.9331                         |
+| 5.9574 | 840  | 8.8674        | 11.1829         | 0.9329                         |
+| 6.0993 | 860  | 8.0564        | 11.3401         | 0.9367                         |
+| 6.2411 | 880  | 8.1608        | 11.3323         | 0.9370                         |
+| 6.3830 | 900  | 8.2702        | 11.3146         | 0.9370                         |
+| 6.5248 | 920  | 8.3711        | 11.2561         | 0.9372                         |
+| 6.6667 | 940  | 8.421         | 11.2558         | 0.9354                         |
+| 6.8085 | 960  | 8.4125        | 11.1738         | 0.9384                         |
+| 6.9504 | 980  | 8.42          | 11.0996         | 0.9415                         |
+
+
+### Framework Versions
+- Python: 3.11.11
+- Sentence Transformers: 3.3.1
+- Transformers: 4.47.1
+- PyTorch: 2.5.1+cu121
+- 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": "/content/models/SciGenSBert/checkpoint-980",
+  "activation_function": "swiglu",
+  "architectures": [
+    "NomicBertModel"
+  ],
+  "attn_pdrop": 0.0,
+  "auto_map": {
+    "AutoConfig": "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+cu121"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

configuration_hf_nomic_bert.py

@@ -0,0 +1,56 @@
+from transformers import GPT2Config
+
+
+class NomicBertConfig(GPT2Config):
+    model_type = "nomic_bert"
+
+    def __init__(
+        self,
+        prenorm=False,
+        parallel_block=False,
+        parallel_block_tied_norm=False,
+        rotary_emb_fraction=0.0,
+        fused_dropout_add_ln=False,
+        fused_bias_fc=False,
+        use_flash_attn=False,
+        use_xentropy=False,
+        qkv_proj_bias=True,
+        rotary_emb_base=10_000,
+        rotary_emb_scale_base=None,
+        rotary_emb_interleaved=False,
+        mlp_fc1_bias=True,
+        mlp_fc2_bias=True,
+        use_rms_norm=False,
+        causal=False,
+        type_vocab_size=2,
+        dense_seq_output=True,
+        pad_vocab_size_multiple=1,
+        tie_word_embeddings=True,
+        rotary_scaling_factor=None,
+        max_trained_positions=2048,
+        **kwargs,
+    ):
+        self.prenorm = prenorm
+        self.parallel_block = parallel_block
+        self.parallel_block_tied_norm = parallel_block_tied_norm
+        self.rotary_emb_fraction = rotary_emb_fraction
+        self.tie_word_embeddings = tie_word_embeddings
+        self.fused_dropout_add_ln = fused_dropout_add_ln
+        self.fused_bias_fc = fused_bias_fc
+        self.use_flash_attn = use_flash_attn
+        self.use_xentropy = use_xentropy
+        self.qkv_proj_bias = qkv_proj_bias
+        self.rotary_emb_base = rotary_emb_base
+        self.rotary_emb_scale_base = rotary_emb_scale_base
+        self.rotary_emb_interleaved = rotary_emb_interleaved
+        self.mlp_fc1_bias = mlp_fc1_bias
+        self.mlp_fc2_bias = mlp_fc2_bias
+        self.use_rms_norm = use_rms_norm
+        self.causal = causal
+        self.type_vocab_size = type_vocab_size
+        self.dense_seq_output = dense_seq_output
+        self.pad_vocab_size_multiple = pad_vocab_size_multiple
+        self.rotary_scaling_factor = rotary_scaling_factor
+        self.max_trained_positions = max_trained_positions
+
+        super().__init__(**kwargs)

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:bbc7d9c53386e9f503d7a727f7ee1ddc54653f4007ef15942f62f431b6cb8725
+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 +1,63 @@
+{
+  "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]",
+  "max_length": 8192,
+  "model_max_length": 8192,
+  "pad_to_multiple_of": null,
+  "pad_token": "[PAD]",
+  "pad_token_type_id": 0,
+  "padding_side": "right",
+  "sep_token": "[SEP]",
+  "stride": 0,
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "[UNK]"
+}