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1_Pooling/config.json

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+{
+  "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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+---
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:56322
+- loss:MultipleNegativesRankingLoss
+base_model: allenai/specter2_base
+widget:
+- source_sentence: Telehealth Uptake
+  sentences:
+  - 'Telepsychiatry and face-to-face psychiatric consultations during the first year
+    of the COVID-19 pandemic in Australia: patients being heard and seen. '
+  - 'The Determinants of Telehealth Provision: Empirical Evidence from OECD Countries. '
+  - 'Transvenous cardioverter-defibrillator placement via a persistent left superior
+    vena cava. '
+- source_sentence: Neonatal Mortality Rate in Nairobi
+  sentences:
+  - 'Metabolic responses to BRL37344 and clenbuterol in soleus muscle and C2C12 cells
+    via different atypical pharmacologies and beta2-adrenoceptor mechanisms. '
+  - 'A prospective study of neonatal deaths in Nairobi, Kenya. '
+  - 'Nairobi study. '
+- source_sentence: Angiotensin II receptor blockade in IgA nephropathy
+  sentences:
+  - 'Use of the index of ideality of correlation to improve aquatic solubility model. '
+  - 'The role of angiotensin II receptor blockers in preventing the progression of
+    renal disease in patients with type 2 diabetes. '
+  - 'The effect of angiotensin type 1 receptor blockade on adhesion molecules in patients
+    with IgA nephropathy. '
+- source_sentence: Immunopotentiator therapy outcomes in cancer patients
+  sentences:
+  - '[Immunotherapy of terminal-stage malignant tumors with the immunopotentiator
+    OK-432--a comparison between SU-PS test-responder and -nonresponder patients]. '
+  - 'Investigation of modified platelet-rich plasma (mPRP) in promoting the proliferation
+    and differentiation of dental pulp stem cells from deciduous teeth. '
+  - 'Trends and challenges in immuno-oncology trials. '
+- source_sentence: Community-based management of neonatal sepsis
+  sentences:
+  - 'Lessons from implementation research on community management of Possible Serious
+    Bacterial Infection (PSBI) in young infants (0-59 days), when the referral is
+    not feasible in Palwal district of Haryana, India. '
+  - 'Community management of severe pneumonia in children. '
+  - 'Homo sapiens in the Americas. Overview of the earliest human expansion in the
+    New World. '
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+---
+
+# SentenceTransformer based on allenai/specter2_base
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [allenai/specter2_base](https://huggingface.co/allenai/specter2_base) on the json 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:** [allenai/specter2_base](https://huggingface.co/allenai/specter2_base) <!-- at revision 3447645e1def9117997203454fa4495937bfbd83 -->
+- **Maximum Sequence Length:** 512 tokens
+- **Output Dimensionality:** 768 dimensions
+- **Similarity Function:** Cosine Similarity
+- **Training Dataset:**
+    - json
+<!-- - **Language:** Unknown -->
+<!-- - **License:** Unknown -->
+
+### Model Sources
+
+- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
+- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
+- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
+
+### Full Model Architecture
+
+```
+SentenceTransformer(
+  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: PeftModelForFeatureExtraction 
+  (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("sentence_transformers_model_id")
+# Run inference
+sentences = [
+    'Community-based management of neonatal sepsis',
+    'Lessons from implementation research on community management of Possible Serious Bacterial Infection (PSBI) in young infants (0-59 days), when the referral is not feasible in Palwal district of Haryana, India. ',
+    'Community management of severe pneumonia in children. ',
+]
+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.*
+-->
+
+<!--
+## 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
+
+#### json
+
+* Dataset: json
+* Size: 56,322 training samples
+* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | anchor                                                                           | positive                                                                           | negative                                                                          |
+  |:--------|:---------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
+  | type    | string                                                                           | string                                                                             | string                                                                            |
+  | details | <ul><li>min: 3 tokens</li><li>mean: 7.57 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 20.41 tokens</li><li>max: 122 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 12.46 tokens</li><li>max: 47 tokens</li></ul> |
+* Samples:
+  | anchor                                              | positive                                                                                                                                                                               | negative                                                                                                            |
+  |:----------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------|
+  | <code>Karyomorphology</code>                        | <code>Karyomorphology of Taiwanese Begonia (Begoniaceae): taxonomic implications. </code>                                                                                              | <code>Spectral karyotyping. </code>                                                                                 |
+  | <code>Topical anesthetic efficacy predictors</code> | <code>Determinants of success and failure of EMLA. </code>                                                                                                                             | <code>Topical anesthetics update: EMLA and beyond. </code>                                                          |
+  | <code>Laser-induced bacteremia</code>               | <code>[Bacteremia following endoscopic laser therapy. Incidence of bacteremia and infection following laser coagulation of stenosing processes of the gastrointestinal tract]. </code> | <code>Bacterial infections following non-ablative fractional laser treatment: a case series and discussion. </code> |
+* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "cos_sim"
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `per_device_train_batch_size`: 16
+- `gradient_accumulation_steps`: 48
+- `learning_rate`: 0.0003
+- `num_train_epochs`: 1
+- `lr_scheduler_type`: cosine_with_restarts
+- `warmup_ratio`: 0.1
+- `bf16`: True
+- `batch_sampler`: no_duplicates
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: no
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 16
+- `per_device_eval_batch_size`: 8
+- `per_gpu_train_batch_size`: None
+- `per_gpu_eval_batch_size`: None
+- `gradient_accumulation_steps`: 48
+- `eval_accumulation_steps`: None
+- `torch_empty_cache_steps`: None
+- `learning_rate`: 0.0003
+- `weight_decay`: 0.0
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `max_grad_norm`: 1.0
+- `num_train_epochs`: 1
+- `max_steps`: -1
+- `lr_scheduler_type`: cosine_with_restarts
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.1
+- `warmup_steps`: 0
+- `log_level`: passive
+- `log_level_replica`: warning
+- `log_on_each_node`: True
+- `logging_nan_inf_filter`: True
+- `save_safetensors`: True
+- `save_on_each_node`: False
+- `save_only_model`: False
+- `restore_callback_states_from_checkpoint`: False
+- `no_cuda`: False
+- `use_cpu`: False
+- `use_mps_device`: False
+- `seed`: 42
+- `data_seed`: None
+- `jit_mode_eval`: False
+- `use_ipex`: False
+- `bf16`: True
+- `fp16`: False
+- `fp16_opt_level`: O1
+- `half_precision_backend`: auto
+- `bf16_full_eval`: False
+- `fp16_full_eval`: False
+- `tf32`: None
+- `local_rank`: 0
+- `ddp_backend`: None
+- `tpu_num_cores`: None
+- `tpu_metrics_debug`: False
+- `debug`: []
+- `dataloader_drop_last`: False
+- `dataloader_num_workers`: 0
+- `dataloader_prefetch_factor`: None
+- `past_index`: -1
+- `disable_tqdm`: False
+- `remove_unused_columns`: True
+- `label_names`: None
+- `load_best_model_at_end`: False
+- `ignore_data_skip`: False
+- `fsdp`: []
+- `fsdp_min_num_params`: 0
+- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
+- `fsdp_transformer_layer_cls_to_wrap`: None
+- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
+- `deepspeed`: None
+- `label_smoothing_factor`: 0.0
+- `optim`: adamw_torch
+- `optim_args`: None
+- `adafactor`: False
+- `group_by_length`: False
+- `length_column_name`: length
+- `ddp_find_unused_parameters`: None
+- `ddp_bucket_cap_mb`: None
+- `ddp_broadcast_buffers`: False
+- `dataloader_pin_memory`: True
+- `dataloader_persistent_workers`: False
+- `skip_memory_metrics`: True
+- `use_legacy_prediction_loop`: False
+- `push_to_hub`: False
+- `resume_from_checkpoint`: None
+- `hub_model_id`: None
+- `hub_strategy`: every_save
+- `hub_private_repo`: 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`: no_duplicates
+- `multi_dataset_batch_sampler`: proportional
+
+</details>
+
+### Training Logs
+| Epoch  | Step | Training Loss |
+|:------:|:----:|:-------------:|
+| 0.0136 | 1    | 83.9795       |
+| 0.0273 | 2    | 83.385        |
+| 0.0409 | 3    | 81.5195       |
+| 0.0545 | 4    | 82.4763       |
+| 0.0682 | 5    | 77.9581       |
+| 0.0818 | 6    | 75.8043       |
+| 0.0954 | 7    | 74.3289       |
+| 0.1091 | 8    | 67.4518       |
+| 0.1227 | 9    | 63.8553       |
+| 0.1363 | 10   | 59.4602       |
+| 0.1500 | 11   | 54.0893       |
+| 0.1636 | 12   | 51.1418       |
+| 0.1772 | 13   | 50.7358       |
+| 0.1909 | 14   | 50.7782       |
+| 0.2045 | 15   | 47.9497       |
+| 0.2181 | 16   | 51.9096       |
+| 0.2318 | 17   | 46.8841       |
+| 0.2454 | 18   | 49.1223       |
+| 0.2590 | 19   | 49.3476       |
+| 0.2726 | 20   | 46.8873       |
+| 0.2863 | 21   | 45.627        |
+| 0.2999 | 22   | 45.4817       |
+| 0.3135 | 23   | 41.4739       |
+| 0.3272 | 24   | 41.5653       |
+| 0.3408 | 25   | 43.989        |
+| 0.3544 | 26   | 42.719        |
+| 0.3681 | 27   | 39.3142       |
+| 0.3817 | 28   | 37.5277       |
+| 0.3953 | 29   | 41.4385       |
+| 0.4090 | 30   | 36.9825       |
+| 0.4226 | 31   | 33.9214       |
+| 0.4362 | 32   | 37.8196       |
+| 0.4499 | 33   | 30.578        |
+| 0.4635 | 34   | 33.6526       |
+| 0.4771 | 35   | 31.7205       |
+| 0.4908 | 36   | 33.5226       |
+| 0.5044 | 37   | 33.491        |
+| 0.5180 | 38   | 30.3925       |
+| 0.5317 | 39   | 32.6365       |
+| 0.5453 | 40   | 29.2933       |
+| 0.5589 | 41   | 31.0618       |
+| 0.5726 | 42   | 32.1394       |
+| 0.5862 | 43   | 33.1907       |
+| 0.5998 | 44   | 30.6758       |
+| 0.6135 | 45   | 29.0445       |
+| 0.6271 | 46   | 30.2431       |
+| 0.6407 | 47   | 29.6209       |
+| 0.6544 | 48   | 28.1471       |
+| 0.6680 | 49   | 28.9184       |
+| 0.6816 | 50   | 27.3557       |
+| 0.6953 | 51   | 29.4244       |
+| 0.7089 | 52   | 28.983        |
+| 0.7225 | 53   | 27.1205       |
+| 0.7362 | 54   | 28.0307       |
+| 0.7498 | 55   | 31.0242       |
+| 0.7634 | 56   | 26.7353       |
+| 0.7771 | 57   | 28.5273       |
+| 0.7907 | 58   | 27.497        |
+| 0.8043 | 59   | 27.6916       |
+| 0.8179 | 60   | 26.049        |
+| 0.8316 | 61   | 27.925        |
+| 0.8452 | 62   | 26.8952       |
+| 0.8588 | 63   | 28.7678       |
+| 0.8725 | 64   | 28.4479       |
+| 0.8861 | 65   | 26.7105       |
+| 0.8997 | 66   | 26.0126       |
+| 0.9134 | 67   | 27.6992       |
+| 0.9270 | 68   | 27.0397       |
+| 0.9406 | 69   | 26.4259       |
+| 0.9543 | 70   | 27.6363       |
+| 0.9679 | 71   | 26.9392       |
+| 0.9815 | 72   | 24.69         |
+| 0.9952 | 73   | 24.5999       |
+
+
+### Framework Versions
+- Python: 3.12.3
+- Sentence Transformers: 3.3.1
+- Transformers: 4.49.0
+- PyTorch: 2.5.1
+- Accelerate: 1.2.1
+- Datasets: 2.19.0
+- 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",
+}
+```
+
+#### 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

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+{
+  "_name_or_path": "allenai/specter2_base",
+  "adapters": {
+    "adapters": {},
+    "config_map": {},
+    "fusion_config_map": {},
+    "fusions": {}
+  },
+  "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": "bfloat16",
+  "transformers_version": "4.49.0",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 31090
+}

config_sentence_transformers.json

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+{
+  "__version__": {
+    "sentence_transformers": "3.3.1",
+    "transformers": "4.49.0",
+    "pytorch": "2.5.1"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:32f1407fe668901c8dcce35b69a71363eddc0d23a6ea92ff11351e764697f8fa
+size 219859384

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

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+{
+  "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
+  },
+  "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

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+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "101": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "102": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "103": {
+      "content": "[SEP]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "104": {
+      "content": "[MASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": false,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "extra_special_tokens": {},
+  "mask_token": "[MASK]",
+  "model_max_length": 1000000000000000019884624838656,
+  "never_split": null,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}