Initial version

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
+}

README.md

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+---
+pipeline_tag: sentence-similarity
+tags:
+- sentence-transformers
+- feature-extraction
+- sentence-similarity
+- transformers
+language: en
+license: apache-2.0
+---
+
+# PubMedBERT Embeddings
+
+This is a [PubMedBERT-base](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext) model fined-tuned using [sentence-transformers](https://www.SBERT.net). It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. The training dataset was generated using a random sample of [PubMed](https://pubmed.ncbi.nlm.nih.gov/) title-abstract pairs along with similar title pairs.
+
+PubMedBERT Embeddings produces higher quality embeddings than generalized models for medical literature. Further fine-tuning for a medical subdomain will result in even better performance.
+
+## Usage (txtai)
+
+This model can be used to build embeddings databases with [txtai](https://github.com/neuml/txtai) for semantic search and/or as a knowledge source for retrieval augmented generation (RAG).
+
+```python
+import txtai
+
+embeddings = txtai.Embeddings(path="neuml/pubmedbert-base-embeddings", content=True)
+embeddings.index(documents())
+
+# Run a query
+embeddings.search("query to run")
+```
+
+## Usage (Sentence-Transformers)
+
+Alternatively, the model can be loaded with [sentence-transformers](https://www.SBERT.net).
+
+```python
+from sentence_transformers import SentenceTransformer
+sentences = ["This is an example sentence", "Each sentence is converted"]
+
+model = SentenceTransformer("neuml/pubmedbert-base-embeddings")
+embeddings = model.encode(sentences)
+print(embeddings)
+```
+
+## Usage (Hugging Face Transformers)
+
+The model can also be used directly with Transformers. 
+
+```python
+from transformers import AutoTokenizer, AutoModel
+import torch
+
+# Mean Pooling - Take attention mask into account for correct averaging
+def meanpooling(output, mask):
+    embeddings = output[0] # First element of model_output contains all token embeddings
+    mask = mask.unsqueeze(-1).expand(embeddings.size()).float()
+    return torch.sum(embeddings * mask, 1) / torch.clamp(mask.sum(1), min=1e-9)
+
+# Sentences we want sentence embeddings for
+sentences = ['This is an example sentence', 'Each sentence is converted']
+
+# Load model from HuggingFace Hub
+tokenizer = AutoTokenizer.from_pretrained("neuml/pubmedbert-base-embeddings")
+model = AutoModel.from_pretrained("neuml/pubmedbert-base-embeddings")
+
+# Tokenize sentences
+inputs = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
+
+# Compute token embeddings
+with torch.no_grad():
+    output = model(**inputs)
+
+# Perform pooling. In this case, mean pooling.
+embeddings = meanpooling(output, inputs['attention_mask'])
+
+print("Sentence embeddings:")
+print(embeddings)
+```
+
+## Evaluation Results
+
+Performance of this model compared to the top base models on the [MTEB leaderboard](https://huggingface.co/spaces/mteb/leaderboard) is shown below. A popular smaller model was also evaluated along with the most downloaded PubMed similarity model on the Hugging Face Hub.
+
+The following datasets were used to evaluate model performance.
+
+- [PubMed QA](https://huggingface.co/datasets/pubmed_qa)
+  - Subset: pqa_labeled, Split: train, Pair: (question, long_answer)
+- [PubMed Subset](https://huggingface.co/datasets/zxvix/pubmed_subset_new)
+  - Split: test, Pair: (title, text)
+- [PubMed Summary](https://huggingface.co/datasets/scientific_papers)
+  - Subset: pubmed, Split: validation, Pair: (article, abstract)
+
+Evaluation results are shown below. The [Pearson correlation coefficient](https://en.wikipedia.org/wiki/Pearson_correlation_coefficient) is used as the evaluation metric.
+
+| Model                                                                         | PubMed QA | PubMed Subset | PubMed Summary | Average   |
+| ----------------------------------------------------------------------------- | --------- | ------------- | -------------- | --------- | 
+| [all-MiniLM-L6-v2](https://hf.co/sentence-transformers/all-MiniLM-L6-v2)      | 90.40     | 95.86         | 94.07          | 93.44     | 
+| [bge-base-en-v1.5](https://hf.co/BAAI/bge-large-en-v1.5)                      | 91.02     | 95.60         | 94.49          | 93.70     | 
+| [gte-base](https://hf.co/thenlper/gte-base)                                   | 92.97     | 96.83         | 96.24          | 95.35     | 
+| [pubmedbert-base-embeddings](https://hf.co/neuml/pubmedbert-base-embeddings)  | **93.27** | **97.07**     | **96.58**      | **95.64** |
+| [S-PubMedBert-MS-MARCO](https://hf.co/pritamdeka/S-PubMedBert-MS-MARCO)       | 90.86     | 93.33         | 93.54          | 92.58     | 
+
+## Training
+
+The model was trained with the parameters:
+
+**DataLoader**:
+
+`torch.utils.data.dataloader.DataLoader` of length 20191 with parameters:
+```
+{'batch_size': 24, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
+```
+
+**Loss**:
+
+`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
+  ```
+  {'scale': 20.0, 'similarity_fct': 'cos_sim'}
+  ```
+
+Parameters of the fit()-Method:
+```
+{
+    "epochs": 1,
+    "evaluation_steps": 500,
+    "evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
+    "max_grad_norm": 1,
+    "optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
+    "optimizer_params": {
+        "lr": 2e-05
+    },
+    "scheduler": "WarmupLinear",
+    "steps_per_epoch": null,
+    "warmup_steps": 10000,
+    "weight_decay": 0.01
+}
+```
+
+## 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})
+)
+```
+
+## More Information
+
+Read more about this model and how it was built in [this article](https://medium.com/neuml/embeddings-for-medical-literature-74dae6abf5e0).

added_tokens.json

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+{
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+  "[MASK]": 4,
+  "[PAD]": 0,
+  "[SEP]": 3,
+  "[UNK]": 1
+}

config.json

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+{
+  "_name_or_path": "microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext",
+  "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": "4.34.0",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

config_sentence_transformers.json

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+{
+  "__version__": {
+    "sentence_transformers": "2.2.2",
+    "transformers": "4.34.0",
+    "pytorch": "2.0.1+cu117"
+  }
+}

modules.json

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+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  }
+]

pytorch_model.bin

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sentence_bert_config.json

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+{
+  "max_seq_length": 512,
+  "do_lower_case": false
+}

similarity_evaluation_results.csv

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+epoch,steps,cosine_pearson,cosine_spearman,euclidean_pearson,euclidean_spearman,manhattan_pearson,manhattan_spearman,dot_pearson,dot_spearman
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special_tokens_map.json

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+{
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+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
+}

tokenizer_config.json

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+{
+  "added_tokens_decoder": {
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+      "lstrip": false,
+      "normalized": false,
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+      "special": true
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+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
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+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}