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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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+---
+base_model: allenai/specter2_base
+library_name: sentence-transformers
+pipeline_tag: sentence-similarity
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:8705
+- loss:MultipleNegativesRankingLoss
+widget:
+- source_sentence: Vaccine Administration in High-Risk Groups
+  sentences:
+  - '[V+: strategies improving vaccination coverage among children with chronic diseases]. '
+  - 'Medical writer welcomes advice on working with medical writers. '
+  - 'Vaccination management. '
+- source_sentence: Eosinophil recruitment and STAT6 signalling pathway in nematode
+    infections
+  sentences:
+  - 'The roles of eotaxin and the STAT6 signalling pathway in eosinophil recruitment
+    and host resistance to the nematodes Nippostrongylus brasiliensis and Heligmosomoides
+    bakeri. '
+  - 'ABO blood groups from Palamau, Bihar, India. '
+  - 'Both stat5a and stat5b are required for antigen-induced eosinophil and T-cell
+    recruitment into the tissue. '
+- source_sentence: Constitutional Medicine Status
+  sentences:
+  - '[Present status of constitutional medicine]. '
+  - 'Convergence of submodality-specific input onto neurons in primary somatosensory
+    cortex. '
+  - 'The link between health and wellbeing and constitutional recognition. '
+- source_sentence: Telehealth challenges
+  sentences:
+  - '[Technological transformations and evolution of the medical practice: current
+    status, issues and perspectives for the development of telemedicine]. '
+  - 'The untapped potential of Telehealth. '
+  - 'Enhanced chartreusin solubility by hydroxybenzoate hydrotropy. '
+- source_sentence: Kawasaki disease immunoprophylaxis
+  sentences:
+  - '[Effect of immunoglobulin in the prevention of coronary artery aneurysms in Kawasaki
+    disease]. '
+  - 'Management of Kawasaki disease. '
+  - 'IgA anti-epidermal transglutaminase antibodies in dermatitis herpetiformis and
+    pediatric celiac disease. '
+---
+
+# 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 tokens
+- **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: BertModel 
+  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+)
+```
+
+## Usage
+
+### Direct Usage (Sentence Transformers)
+
+First install the Sentence Transformers library:
+
+```bash
+pip install -U sentence-transformers
+```
+
+Then you can load this model and run inference.
+```python
+from sentence_transformers import SentenceTransformer
+
+# Download from the 🤗 Hub
+model = SentenceTransformer("sentence_transformers_model_id")
+# Run inference
+sentences = [
+    'Kawasaki disease immunoprophylaxis',
+    '[Effect of immunoglobulin in the prevention of coronary artery aneurysms in Kawasaki disease]. ',
+    'Management of Kawasaki disease. ',
+]
+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: 8,705 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: 4 tokens</li><li>mean: 7.6 tokens</li><li>max: 18 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 19.26 tokens</li><li>max: 42 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 11.72 tokens</li><li>max: 46 tokens</li></ul> |
+* Samples:
+  | anchor                                                                               | positive                                                                                                                                                          | negative                                                            |
+  |:-------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------|
+  | <code>Telehealth challenges</code>                                                   | <code>[Technological transformations and evolution of the medical practice: current status, issues and perspectives for the development of telemedicine]. </code> | <code>The untapped potential of Telehealth. </code>                 |
+  | <code>Racial disparities in mental health treatment</code>                           | <code>Relationships between stigma, depression, and treatment in white and African American primary care patients. </code>                                        | <code>Mental Health Care Disparities Now and in the Future. </code> |
+  | <code>Iatrogenic hyperkalemia in elderly patients with cardiovascular disease</code> | <code>Iatrogenic hyperkalemia as a serious problem in therapy of cardiovascular diseases in elderly patients. </code>                                             | <code>The cardiovascular implications of hypokalemia. </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`: 32
+- `per_device_eval_batch_size`: 32
+- `learning_rate`: 2e-05
+- `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`: 32
+- `per_device_eval_batch_size`: 32
+- `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`: 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`: False
+- `hub_always_push`: False
+- `gradient_checkpointing`: False
+- `gradient_checkpointing_kwargs`: None
+- `include_inputs_for_metrics`: False
+- `eval_do_concat_batches`: True
+- `fp16_backend`: auto
+- `push_to_hub_model_id`: None
+- `push_to_hub_organization`: None
+- `mp_parameters`: 
+- `auto_find_batch_size`: False
+- `full_determinism`: False
+- `torchdynamo`: None
+- `ray_scope`: last
+- `ddp_timeout`: 1800
+- `torch_compile`: False
+- `torch_compile_backend`: None
+- `torch_compile_mode`: None
+- `dispatch_batches`: None
+- `split_batches`: None
+- `include_tokens_per_second`: False
+- `include_num_input_tokens_seen`: False
+- `neftune_noise_alpha`: None
+- `optim_target_modules`: None
+- `batch_eval_metrics`: False
+- `eval_on_start`: False
+- `use_liger_kernel`: False
+- `eval_use_gather_object`: False
+- `batch_sampler`: no_duplicates
+- `multi_dataset_batch_sampler`: proportional
+
+</details>
+
+### Training Logs
+| Epoch  | Step | Training Loss |
+|:------:|:----:|:-------------:|
+| 0.0110 | 1    | 2.9861        |
+| 0.0220 | 2    | 2.9379        |
+| 0.0330 | 3    | 3.0613        |
+| 0.0440 | 4    | 2.8081        |
+| 0.0549 | 5    | 2.6516        |
+| 0.0659 | 6    | 2.3688        |
+| 0.0769 | 7    | 2.0502        |
+| 0.0879 | 8    | 1.7557        |
+| 0.0989 | 9    | 1.5316        |
+| 0.1099 | 10   | 1.2476        |
+| 0.1209 | 11   | 1.1529        |
+| 0.1319 | 12   | 0.9483        |
+| 0.1429 | 13   | 0.7187        |
+| 0.1538 | 14   | 0.6824        |
+| 0.1648 | 15   | 0.593         |
+| 0.1758 | 16   | 0.4593        |
+| 0.1868 | 17   | 0.3737        |
+| 0.1978 | 18   | 0.5082        |
+| 0.2088 | 19   | 0.4232        |
+| 0.2198 | 20   | 0.3089        |
+| 0.2308 | 21   | 0.2057        |
+| 0.2418 | 22   | 0.2358        |
+| 0.2527 | 23   | 0.2291        |
+| 0.2637 | 24   | 0.2707        |
+| 0.2747 | 25   | 0.1359        |
+| 0.2857 | 26   | 0.2294        |
+| 0.2967 | 27   | 0.157         |
+| 0.3077 | 28   | 0.0678        |
+| 0.3187 | 29   | 0.1022        |
+| 0.3297 | 30   | 0.0713        |
+| 0.3407 | 31   | 0.0899        |
+| 0.3516 | 32   | 0.1385        |
+| 0.3626 | 33   | 0.0809        |
+| 0.3736 | 34   | 0.1053        |
+| 0.3846 | 35   | 0.0925        |
+| 0.3956 | 36   | 0.0675        |
+| 0.4066 | 37   | 0.0841        |
+| 0.4176 | 38   | 0.0366        |
+| 0.4286 | 39   | 0.0768        |
+| 0.4396 | 40   | 0.0529        |
+| 0.4505 | 41   | 0.0516        |
+| 0.4615 | 42   | 0.0342        |
+| 0.4725 | 43   | 0.0456        |
+| 0.4835 | 44   | 0.0344        |
+| 0.4945 | 45   | 0.1337        |
+| 0.5055 | 46   | 0.0883        |
+| 0.5165 | 47   | 0.0691        |
+| 0.5275 | 48   | 0.0322        |
+| 0.5385 | 49   | 0.0731        |
+| 0.5495 | 50   | 0.0376        |
+| 0.5604 | 51   | 0.0464        |
+| 0.5714 | 52   | 0.0173        |
+| 0.5824 | 53   | 0.0516        |
+| 0.5934 | 54   | 0.0703        |
+| 0.6044 | 55   | 0.0273        |
+| 0.6154 | 56   | 0.0374        |
+| 0.6264 | 57   | 0.0292        |
+| 0.6374 | 58   | 0.1195        |
+| 0.6484 | 59   | 0.0852        |
+| 0.6593 | 60   | 0.0697        |
+| 0.6703 | 61   | 0.0653        |
+| 0.6813 | 62   | 0.0426        |
+| 0.6923 | 63   | 0.0288        |
+| 0.7033 | 64   | 0.0344        |
+| 0.7143 | 65   | 0.104         |
+| 0.7253 | 66   | 0.0251        |
+| 0.7363 | 67   | 0.0095        |
+| 0.7473 | 68   | 0.0208        |
+| 0.7582 | 69   | 0.0814        |
+| 0.7692 | 70   | 0.0813        |
+| 0.7802 | 71   | 0.0508        |
+| 0.7912 | 72   | 0.032         |
+| 0.8022 | 73   | 0.0879        |
+| 0.8132 | 74   | 0.095         |
+| 0.8242 | 75   | 0.0932        |
+| 0.8352 | 76   | 0.0868        |
+| 0.8462 | 77   | 0.0231        |
+| 0.8571 | 78   | 0.0144        |
+| 0.8681 | 79   | 0.0179        |
+| 0.8791 | 80   | 0.0457        |
+| 0.8901 | 81   | 0.0935        |
+| 0.9011 | 82   | 0.0658        |
+| 0.9121 | 83   | 0.0553        |
+| 0.9231 | 84   | 0.003         |
+| 0.9341 | 85   | 0.0036        |
+| 0.9451 | 86   | 0.0034        |
+| 0.9560 | 87   | 0.0032        |
+| 0.9670 | 88   | 0.0026        |
+| 0.9780 | 89   | 0.0042        |
+| 0.9890 | 90   | 0.0024        |
+| 1.0    | 91   | 0.0022        |
+
+
+### Framework Versions
+- Python: 3.9.19
+- Sentence Transformers: 3.1.1
+- Transformers: 4.45.2
+- PyTorch: 2.5.0
+- Accelerate: 1.0.1
+- Datasets: 2.19.0
+- Tokenizers: 0.20.3
+
+## 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

@@ -0,0 +1,31 @@
+{
+  "_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": "float32",
+  "transformers_version": "4.45.2",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 31090
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.1.1",
+    "transformers": "4.45.2",
+    "pytorch": "2.5.0"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": null
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:947aaf825081d24a38f3bf7dfa0d2427cc62a2c776f90deeb9d5f72ed95d6a3e
+size 439696224

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": 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

@@ -0,0 +1,57 @@
+{
+  "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,
+  "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]"
+}