BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext

Maintainer: microsoft

Total Score

165

Last updated 5/28/2024

🌐

PropertyValue
Run this modelRun on HuggingFace
API specView on HuggingFace
Github linkNo Github link provided
Paper linkNo paper link provided

Create account to get full access

or

If you already have an account, we'll log you in

Model overview

The microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext model, previously known as "PubMedBERT (abstracts + full text)", is a large neural language model pretrained from scratch using abstracts from PubMed and full-text articles from PubMedCentral. This model achieves state-of-the-art performance on many biomedical NLP tasks and currently holds the top score on the Biomedical Language Understanding and Reasoning Benchmark.

Similar models include BiomedNLP-BiomedBERT-base-uncased-abstract, a version of the model trained only on PubMed abstracts, as well as the generative BioGPT models developed by Microsoft.

Model inputs and outputs

Inputs

  • Arbitrary biomedical text, such as research paper abstracts or clinical notes

Outputs

  • Contextual representations of the input text that can be used for a variety of downstream biomedical NLP tasks, such as named entity recognition, relation extraction, and question answering.

Capabilities

The BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext model is highly capable at understanding and processing biomedical text. It has been shown to outperform previous models on a range of tasks, including relation extraction from clinical text and question answering about biomedical concepts.

What can I use it for?

This model is well-suited for any biomedical NLP application that requires understanding and reasoning about scientific literature and clinical data. Example use cases include:

  • Extracting insights and relationships from large collections of biomedical papers
  • Answering questions about medical conditions, treatments, and research findings
  • Improving the accuracy of clinical decision support systems
  • Enhancing biomedical text mining and information retrieval

Things to try

One interesting aspect of this model is its ability to leverage both abstracts and full-text articles during pretraining. You could experiment with using the model for different types of biomedical text, such as clinical notes or patient records, and compare the performance to models trained only on abstracts. Additionally, you could explore fine-tuning the model on specific biomedical tasks to see how it compares to other state-of-the-art approaches.



This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!

Related Models

📶

BiomedNLP-BiomedBERT-base-uncased-abstract

microsoft

Total Score

57

BiomedNLP-BiomedBERT-base-uncased-abstract is a biomedical language model developed by Microsoft. It was previously known as "PubMedBERT (abstracts)". This model was pretrained from scratch using abstracts from PubMed, the leading biomedical literature database. Unlike many language models that start from a general-domain corpus and then continue pretraining on domain-specific text, this model was trained entirely on biomedical abstracts. This allows it to better capture the specialized vocabulary and concepts used in the biomedical field. Similar models include BioGPT-Large-PubMedQA, BioGPT-Large, biogpt, and BioMedLM, all of which are biomedical language models trained on domain-specific text. Model inputs and outputs Inputs Text**: The model takes in text data, typically in the form of biomedical abstracts or other domain-specific content. Outputs Encoded text representation**: The model outputs a numerical representation of the input text, which can be used for downstream natural language processing tasks such as text classification, question answering, or named entity recognition. Capabilities BiomedNLP-BiomedBERT-base-uncased-abstract has shown state-of-the-art performance on several biomedical NLP benchmarks, including the Biomedical Language Understanding and Reasoning Benchmark (BLURB). Its specialized pretraining on biomedical abstracts allows it to better capture the nuances of the biomedical domain compared to language models trained on more general text. What can I use it for? The BiomedNLP-BiomedBERT-base-uncased-abstract model can be fine-tuned on a variety of biomedical NLP tasks, such as: Text classification**: Classifying biomedical literature into categories like disease, treatment, or diagnosis. Question answering**: Answering questions about biomedical concepts, treatments, or research findings. Named entity recognition**: Identifying and extracting relevant biomedical entities like drugs, genes, or diseases from text. Researchers and developers in the biomedical and healthcare domains may find this model particularly useful for building advanced natural language processing applications that require a deep understanding of domain-specific terminology and concepts. Things to try One interesting aspect of BiomedNLP-BiomedBERT-base-uncased-abstract is its ability to perform well on biomedical tasks without the need for continued pretraining on general-domain text. This suggests that starting from a model that is already well-versed in the biomedical domain can be more effective than taking a general-purpose model and further pretraining it on biomedical data. Exploring the tradeoffs between these approaches could lead to valuable insights for future model development.

Read more

Updated Invalid Date

🧠

BioGPT-Large

microsoft

Total Score

166

BioGPT is a domain-specific generative Transformer language model pre-trained on large-scale biomedical literature. Developed by Microsoft, BioGPT aims to address the lack of generation ability in existing biomedical language models like BioBERT and PubMedBERT, which have primarily focused on discriminative tasks. Compared to similar models like BioGPT-Large-PubMedQA and BioMedGPT-LM-7B, BioGPT demonstrates strong performance on a variety of biomedical natural language processing tasks, including relation extraction and question answering. Its ability to generate fluent descriptions for biomedical terms also sets it apart. Model Inputs and Outputs Inputs Sequences of continuous biomedical text Outputs Predicted next tokens in a biomedical text sequence, generated in an autoregressive manner Capabilities BioGPT excels at generative tasks in the biomedical domain, such as summarizing research papers, generating new biomedical content, and answering questions about medical concepts. It has been shown to outperform previous models on relation extraction benchmarks like BC5CDR, KD-DTI, and DDI, as well as achieving state-of-the-art performance on the PubMedQA dataset. What Can I Use it For? BioGPT can be utilized in a variety of biomedical applications that require natural language understanding and generation, such as: Generating summaries of research papers or clinical notes Answering questions about medical conditions, treatments, or pharmaceuticals Assisting in the creation of new biomedical content, like educational materials or scientific hypotheses Powering conversational agents for patient support or clinician-patient interactions The model's strong performance on relation extraction tasks also makes it a valuable tool for tasks like drug discovery, disease diagnosis, and knowledge graph construction. Things to Try One interesting aspect of BioGPT is its ability to generate fluent descriptions for biomedical terms, which could be useful for educational or explanatory purposes. Researchers could experiment with prompting the model to generate definitions or explanations for complex medical concepts, and then evaluate the quality and usefulness of the generated text. Additionally, users could fine-tune BioGPT on domain-specific datasets to further improve its performance on specialized biomedical tasks, or explore ways to combine the model's language understanding capabilities with other modalities, such as structured biomedical data, to create more comprehensive AI solutions for the life sciences.

Read more

Updated Invalid Date

🏅

BioGPT-Large-PubMedQA

microsoft

Total Score

97

The BioGPT-Large-PubMedQA model is a domain-specific generative Transformer language model developed by Microsoft and pre-trained on large-scale biomedical literature. This model was created to address the lack of generation ability in previous biomedical language models like BioBERT and PubMedBERT, which have achieved great success on discriminative downstream biomedical tasks but are constrained in their application scope. In contrast, BioGPT-Large-PubMedQA is a generative language model that can be used for a wider range of biomedical NLP tasks. The model outperforms previous models on most tasks, including achieving new state-of-the-art performance on the PubMedQA biomedical question answering task with 78.2% accuracy. Model inputs and outputs Inputs Text**: The model accepts arbitrary text as input, which it uses to generate additional text. Outputs Generated text**: The model outputs generated text that continues or expands upon the input text. The generated text aims to be fluent and coherent in the biomedical domain. Capabilities The BioGPT-Large-PubMedQA model can be used for a variety of biomedical text generation and mining tasks, such as summarizing research papers, answering questions about medical topics, and generating descriptions of biomedical concepts. For example, the model can be prompted to generate a fluent description of a medical term like "chromatography" or "cytotoxicity", demonstrating its ability to produce coherent biomedical text. What can I use it for? Researchers and developers working on biomedical NLP applications can use BioGPT-Large-PubMedQA to enhance their projects. The model's strong performance on tasks like biomedical question answering and its ability to generate high-quality biomedical text make it a valuable tool for building conversational agents, summarization systems, and other biomedical language processing solutions. Things to try One interesting aspect of BioGPT-Large-PubMedQA is its use of a custom tokenizer trained on the PubMed corpus. This allows the model to better represent common biomedical terms as single tokens, rather than splitting them into multiple subword units. Experimenting with the model's handling of domain-specific vocabulary could yield insights into how to effectively adapt language models for specialized tasks and corpora.

Read more

Updated Invalid Date

📉

BioMedLM

stanford-crfm

Total Score

370

BioMedLM is a new 2.7 billion parameter language model trained exclusively on biomedical abstracts and papers from The Pile. This GPT-style model can achieve strong results on a variety of biomedical NLP tasks, including a new state of the art performance of 50.3% accuracy on the MedQA biomedical question answering task. The model was a joint collaboration of Stanford CRFM and MosaicML. Similar models include Meditron-70B, a 70 billion parameter medical language model adapted from Llama-2-70B, and GPT-Neo 2.7B, a 2.7 billion parameter model trained on a diverse dataset by EleutherAI. Model inputs and outputs Inputs Text**: BioMedLM takes in text data, such as questions, prompts, or documents related to the biomedical domain. Outputs Text**: The model generates English-language text in response to the input, such as an answer to a biomedical question or a summary of a document. Capabilities BioMedLM can be used for a variety of biomedical NLP tasks, including question answering, summarization, and generation. It has achieved state-of-the-art performance on the MedQA biomedical question answering task, demonstrating its strong capabilities in this domain. What can I use it for? Researchers and developers working on biomedical NLP applications can use BioMedLM as a foundation model to build upon. The model's strong performance on tasks like question answering and summarization suggests it could be useful for powering intelligent assistants in the healthcare domain, or for automating tasks like literature review and information extraction. However, the model's generation capabilities are still being explored, and the maintainers caution that it should not be used for production-level tasks without further testing and development. Users should be aware of the model's potential biases and limitations, and take appropriate measures to ensure safe and responsible use. Things to try One interesting aspect of BioMedLM is its exclusive training on biomedical data, in contrast to more general language models that are trained on a wider variety of text. This specialized training could allow the model to develop a deeper understanding of biomedical concepts and terminology, which could be particularly useful for tasks like medical question answering or extraction of information from scientific literature. Developers could explore fine-tuning or prompt engineering strategies to leverage this specialized knowledge. Another avenue to explore is the model's generation capabilities. While the maintainers caution against using the model for open-ended generation, there may be opportunities to use it in a more controlled way, such as for generating summaries or snippets of text to assist with tasks like literature review or report writing. Careful monitoring and evaluation would be essential to ensure the safety and reliability of such applications.

Read more

Updated Invalid Date