thellert
/

physbert_cased

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 ---
 library_name: transformers
-tags: []
 ---
-# Model Card for Model ID
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 ---
 library_name: transformers
+tags: [physics, NLP, embedding, sentence-transformer]
 ---
+# Model Card for PhysBERT
+PhysBERT is a specialized text embedding model for physics, designed to improve information retrieval, citation classification, and clustering of physics literature. Trained on 1.2 million physics papers, it outperforms general-purpose models in physics-specific tasks.
+## Model Description
+PhysBERT is a BERT-based text embedding model for physics, fine-tuned using SimCSE for optimized physics-specific performance. This model enables efficient retrieval, categorization, and analysis of physics literature, achieving higher relevance and accuracy on domain-specific NLP tasks. The uncased version can be found [here](https://huggingface.co/thellert/physbert_uncased).
+- **Developed by:** Thorsten Hellert, João Montenegro, Andrea Pollastro
+- **Funded by:** US Department of Energy, Lawrence Berkeley National Laboratory
+- **Model type:** Text embedding model (BERT-based)
+- **Language(s) (NLP):** English
+- **Paper:** [PhysBERT: A Text Embedding Model for Physics Scientific Literature](https://doi.org/10.1063/5.0238090)
+## Training Data
+Trained on a 40GB corpus from arXiv’s physics publications, consisting of 1.2 million documents, refined for scientific accuracy.
+## Training Procedure
+The model was pre-trained using Masked Language Modeling (MLM) and fine-tuned with SimCSE for sentence embeddings.
+## Example of Usage
+```python
+from transformers import AutoTokenizer, AutoModel
+import torch
+# Load PhysBERT tokenizer and model
+tokenizer = AutoTokenizer.from_pretrained("thellert/physbert_cased")
+model = AutoModel.from_pretrained("thellert/physbert_cased")
+# Sample text to embed
+sample_text = "Electrons exhibit both particle and wave-like behavior."
+# Tokenize the input text and pass it through the model
+inputs = tokenizer(sample_text, return_tensors="pt")
+outputs = model(**inputs)
+# Extract the token embeddings
+token_embeddings = outputs.last_hidden_state
+# Drop CLS and SEP tokens, then take the mean for the sentence embedding
+token_embeddings = token_embeddings[:, 1:-1, :]
+sentence_embedding = token_embeddings.mean(dim=1)
+```
+## Citation
+If you find this work useful please consider citing the following paper:
+```
+@article{10.1063/5.0238090,
+    author = {Hellert, Thorsten and Montenegro, João and Pollastro, Andrea},
+    title = "{PhysBERT: A text embedding model for physics scientific literature}",
+    journal = {APL Machine Learning},
+    volume = {2},
+    number = {4},
+    pages = {046105},
+    year = {2024},
+    month = {10},
+    issn = {2770-9019},
+    doi = {10.1063/5.0238090},
+    url = {https://doi.org/10.1063/5.0238090},
+    eprint = {https://pubs.aip.org/aip/aml/article-pdf/doi/10.1063/5.0238090/20227307/046105\_1\_5.0238090.pdf},
+}
+```
+## Model Card Authors
+Thorsten Hellert, João Montenegro, Andrea Pollastro
 ## Model Card Contact
+Thorsten Hellert, Lawrence Berkeley National Laboratory, [email protected]