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llama3-mova-8b

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mova
text-generation
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  1. .DS_Store +0 -0
  2. README.md +59 -3
  3. config.json +56 -0
  4. experts/.DS_Store +0 -0
  5. experts/BiomedCLIP-PubMedBERT_256-vit_base_patch16_224/LICENSE.md +9 -0
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.DS_Store ADDED
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README.md CHANGED
@@ -1,3 +1,59 @@
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- ---
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- license: apache-2.0
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- ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ ---
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+ inference: false
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+ pipeline_tag: image-text-to-text
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+ ---
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+ <br>
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+ <br>
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+
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+ # MoVA-8B Model Card
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+
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+ ## Model details
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+
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+ **Model type:**
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+ MoVA-8B is an open-source multimodal large language model (MLLM), adaptively routing and fusing task-specific vision experts with a coarse-to-fine mechanism.
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+
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+ Vision Encoders: OpenAI-CLIP-336px, DINOv2-giant, Co-DETR-large, SAM-huge, Vary-base, Pix2Struct-large, Deplot-base, and BiomedCLIP-base.
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+
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+ Base LLM: meta-llama/Meta-Llama-3-8B-Instruct
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+
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+ **Paper or resources for more information:**
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+ [[Paper](https://huggingface.co/papers/2404.13046)] [[Code](https://github.com/TempleX98/MoVA)]
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+
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+ ## Usage
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+ You can directly utilize this model as we provide in our [[repository](https://github.com/TempleX98/MoVA)].
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+
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+ ## License
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+ This project utilizes certain datasets and checkpoints that are subject to their respective original licenses. Users must comply with all terms and conditions of these original licenses, including but not limited to the OpenAI Terms of Use for the dataset and the specific licenses for base language models for checkpoints trained using the dataset (e.g. META LLAMA 3 COMMUNITY LICENSE AGREEMENT).
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+
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+ ## Intended use
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+ **Primary intended uses:**
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+ The primary use of MoVA-8B is research on multimodal models and chatbots.
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+
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+ **Primary intended users:**
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+ The primary intended users of the model are researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence.
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+
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+ ## Training dataset
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+ - 15M diverse visual instruction tuning samples for pre-training, including DataComp-1B, ShareGPT4V-PT, Objects365, and MMC-Instruction. Please refer to our paper for more details.
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+ - 2M high-quality instruction data for fine-tuning. We integrate several visual question answering datasets across various domains, such
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+ as DocVQA, ChartQA, InfographicVQA, AI2D, ST-VQA, TextVQA, SynthDoG-en, Geometry3K, PGPS9K, Geo170K, VQA-RAD, and SLAKE into LLaVA-mix-665k. We also encompass equivalent comprehensive captions generated by GPT4-V.
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+
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+ ## Evaluation dataset
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+ We evaluate our model on a wide range of popular MLLM benchmarks.
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+
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+ ### MultiModal Benchmark
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+
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+ | Name | LLM | \#Tokens | MME | MMBench | MMBench-CN | QBench | MathVista | MathVerse | POPE |
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+ |---|---|---|---|---|---|---|---|---|---|
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+ | MoVA-8B | Llama3-8B | 576 | 1595.8 / 347.5 | 75.3 | 67.7 | 70.8 | 37.7 | 21.4 | 89.3 |
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+
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+ ### General & Text-oriented VQA
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+
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+ | Name | LLM | \#Tokens | VQAv2 | GQA | SQA | TextVQA | ChartQA | DocVQA | AI2D |
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+ |---|---|---|---|---|---|---|---|---|---|
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+ | MoVA-8B | Llama3-8B | 576 | 83.5 | 65.2 | 74.7 | 77.1 | 70.5 | 83.4 | 77.0 |
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+
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+ ### Visual Grounding
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+
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+ Name | LLM | \#Tokens | RefCOCO<br>(val) | RefCOCO<br>(testA) | RefCOCO<br>(testB) | RefCOCO+<br>(val) | RefCOCO+<br>(testA) | RefCOCO+<br>(testB) | RefCOCO&#8209;g<br>(val) | RefCOCO&#8209;g<br>(test) |
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+ |---|---|---|---|---|---|---|---|---|---|---|
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+ | MoVA-8B | Llama3-8B | 576 | 92.18 | 94.75 | 88.24 | 88.45 | 92.21 | 82.82 | 90.05 | 90.23 |
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+ "mm_projector_text_encoder": "bert-large-uncased",
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+ "mm_projector_type": "mov_adapter",
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+ "mm_use_im_patch_token": false,
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+ "mm_use_im_start_end": false,
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+ "mm_vision_select_feature": "patch",
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+ "mm_vision_select_layer": -2,
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+ "mm_vision_tower": [
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+ "openai/clip-vit-large-patch14-336",
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+ "facebook/dinov2-large",
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+ "experts/vary-vit-base-patch16-1024",
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+ "experts/codetr-vit-large/pytorch_model.bin",
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+ "google/pix2struct-large",
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+ "google/deplot",
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+ "experts/BiomedCLIP-PubMedBERT_256-vit_base_patch16_224",
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+ "experts/sam-vit-huge/pytorch_model.bin"
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+ ],
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+ "model_type": "mova",
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+ "num_attention_heads": 32,
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+ "num_hidden_layers": 32,
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+ "num_key_value_heads": 8,
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+ MIT License
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+
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+ Copyright (c) Microsoft Corporation
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+
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+ Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
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+
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+ The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
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+
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+ THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
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+ ---
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+ language: en
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+ tags:
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+ - clip
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+ - biology
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+ - medical
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+ license: mit
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+ library_name: open_clip
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+ widget:
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+ - src: https://huggingface.co/microsoft/BiomedCLIP-PubMedBERT_256-vit_base_patch16_224/resolve/main/example_data/biomed_image_classification_example_data/squamous_cell_carcinoma_histopathology.jpeg
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+ candidate_labels: adenocarcinoma histopathology, squamous cell carcinoma histopathology
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+ example_title: squamous cell carcinoma histopathology
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+ - src: >-
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+ https://huggingface.co/microsoft/BiomedCLIP-PubMedBERT_256-vit_base_patch16_224/resolve/main/example_data/biomed_image_classification_example_data/adenocarcinoma_histopathology.jpg
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+ candidate_labels: adenocarcinoma histopathology, squamous cell carcinoma histopathology
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+ example_title: adenocarcinoma histopathology
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+ - src: >-
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+ https://upload.wikimedia.org/wikipedia/commons/5/57/Left-sided_Pleural_Effusion.jpg
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+ candidate_labels: left-sided pleural effusion chest x-ray, right-sided pleural effusion chest x-ray, normal chest x-ray
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+ example_title: left-sided pleural effusion chest x-ray
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+ pipeline_tag: zero-shot-image-classification
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+ ---
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+
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+ # BiomedCLIP-PubMedBERT_256-vit_base_patch16_224
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+
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+ [BiomedCLIP](https://aka.ms/biomedclip-paper) is a biomedical vision-language foundation model that is pretrained on [PMC-15M](https://aka.ms/biomedclip-paper), a dataset of 15 million figure-caption pairs extracted from biomedical research articles in PubMed Central, using contrastive learning.
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+ It uses PubMedBERT as the text encoder and Vision Transformer as the image encoder, with domain-specific adaptations.
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+ It can perform various vision-language processing (VLP) tasks such as cross-modal retrieval, image classification, and visual question answering.
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+ BiomedCLIP establishes new state of the art in a wide range of standard datasets, and substantially outperforms prior VLP approaches:
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+
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+ ![](biomed-vlp-eval.svg)
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+
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+
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+ ## Citation
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+
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+ ```bibtex
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+ @misc{https://doi.org/10.48550/arXiv.2303.00915,
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+ doi = {10.48550/ARXIV.2303.00915},
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+ url = {https://arxiv.org/abs/2303.00915},
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+ author = {Zhang, Sheng and Xu, Yanbo and Usuyama, Naoto and Bagga, Jaspreet and Tinn, Robert and Preston, Sam and Rao, Rajesh and Wei, Mu and Valluri, Naveen and Wong, Cliff and Lungren, Matthew and Naumann, Tristan and Poon, Hoifung},
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+ title = {Large-Scale Domain-Specific Pretraining for Biomedical Vision-Language Processing},
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+ publisher = {arXiv},
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+ year = {2023},
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+ }
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+ ```
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+
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+ ## Model Use
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+
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+ ### How to use
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+
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+ Please refer to this [example notebook](https://aka.ms/biomedclip-example-notebook).
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+
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+ ### Intended Use
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+
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+ This model is intended to be used solely for (I) future research on visual-language processing and (II) reproducibility of the experimental results reported in the reference paper.
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+
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+ #### Primary Intended Use
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+
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+ The primary intended use is to support AI researchers building on top of this work. BiomedCLIP and its associated models should be helpful for exploring various biomedical VLP research questions, especially in the radiology domain.
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+
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+ #### Out-of-Scope Use
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+
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+ **Any** deployed use case of the model --- commercial or otherwise --- is currently out of scope. Although we evaluated the models using a broad set of publicly-available research benchmarks, the models and evaluations are not intended for deployed use cases. Please refer to [the associated paper](https://aka.ms/biomedclip-paper) for more details.
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+
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+ ## Data
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+
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+ This model builds upon [PMC-15M dataset](https://aka.ms/biomedclip-paper), which is a large-scale parallel image-text dataset for biomedical vision-language processing. It contains 15 million figure-caption pairs extracted from biomedical research articles in PubMed Central. It covers a diverse range of biomedical image types, such as microscopy, radiography, histology, and more.
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+
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+ ## Limitations
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+
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+ This model was developed using English corpora, and thus can be considered English-only.
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+
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+ ## Further information
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+
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+ Please refer to the corresponding paper, ["Large-Scale Domain-Specific Pretraining for Biomedical Vision-Language Processing"](https://aka.ms/biomedclip-paper) for additional details on the model training and evaluation.
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1983
+ "rstrip": false,
1984
+ "single_word": false,
1985
+ "special": true
1986
+ },
1987
+ "128248": {
1988
+ "content": "<|reserved_special_token_243|>",
1989
+ "lstrip": false,
1990
+ "normalized": false,
1991
+ "rstrip": false,
1992
+ "single_word": false,
1993
+ "special": true
1994
+ },
1995
+ "128249": {
1996
+ "content": "<|reserved_special_token_244|>",
1997
+ "lstrip": false,
1998
+ "normalized": false,
1999
+ "rstrip": false,
2000
+ "single_word": false,
2001
+ "special": true
2002
+ },
2003
+ "128250": {
2004
+ "content": "<|reserved_special_token_245|>",
2005
+ "lstrip": false,
2006
+ "normalized": false,
2007
+ "rstrip": false,
2008
+ "single_word": false,
2009
+ "special": true
2010
+ },
2011
+ "128251": {
2012
+ "content": "<|reserved_special_token_246|>",
2013
+ "lstrip": false,
2014
+ "normalized": false,
2015
+ "rstrip": false,
2016
+ "single_word": false,
2017
+ "special": true
2018
+ },
2019
+ "128252": {
2020
+ "content": "<|reserved_special_token_247|>",
2021
+ "lstrip": false,
2022
+ "normalized": false,
2023
+ "rstrip": false,
2024
+ "single_word": false,
2025
+ "special": true
2026
+ },
2027
+ "128253": {
2028
+ "content": "<|reserved_special_token_248|>",
2029
+ "lstrip": false,
2030
+ "normalized": false,
2031
+ "rstrip": false,
2032
+ "single_word": false,
2033
+ "special": true
2034
+ },
2035
+ "128254": {
2036
+ "content": "<|reserved_special_token_249|>",
2037
+ "lstrip": false,
2038
+ "normalized": false,
2039
+ "rstrip": false,
2040
+ "single_word": false,
2041
+ "special": true
2042
+ },
2043
+ "128255": {
2044
+ "content": "<|reserved_special_token_250|>",
2045
+ "lstrip": false,
2046
+ "normalized": false,
2047
+ "rstrip": false,
2048
+ "single_word": false,
2049
+ "special": true
2050
+ }
2051
+ },
2052
+ "additional_special_tokens": [
2053
+ "<|begin_of_text|>",
2054
+ "<|end_of_text|>",
2055
+ "<|reserved_special_token_0|>",
2056
+ "<|reserved_special_token_1|>",
2057
+ "<|reserved_special_token_2|>",
2058
+ "<|reserved_special_token_3|>",
2059
+ "<|start_header_id|>",
2060
+ "<|end_header_id|>",
2061
+ "<|reserved_special_token_4|>",
2062
+ "<|eot_id|>",
2063
+ "<|reserved_special_token_5|>",
2064
+ "<|reserved_special_token_6|>",
2065
+ "<|reserved_special_token_7|>",
2066
+ "<|reserved_special_token_8|>",
2067
+ "<|reserved_special_token_9|>",
2068
+ "<|reserved_special_token_10|>",
2069
+ "<|reserved_special_token_11|>",
2070
+ "<|reserved_special_token_12|>",
2071
+ "<|reserved_special_token_13|>",
2072
+ "<|reserved_special_token_14|>",
2073
+ "<|reserved_special_token_15|>",
2074
+ "<|reserved_special_token_16|>",
2075
+ "<|reserved_special_token_17|>",
2076
+ "<|reserved_special_token_18|>",
2077
+ "<|reserved_special_token_19|>",
2078
+ "<|reserved_special_token_20|>",
2079
+ "<|reserved_special_token_21|>",
2080
+ "<|reserved_special_token_22|>",
2081
+ "<|reserved_special_token_23|>",
2082
+ "<|reserved_special_token_24|>",
2083
+ "<|reserved_special_token_25|>",
2084
+ "<|reserved_special_token_26|>",
2085
+ "<|reserved_special_token_27|>",
2086
+ "<|reserved_special_token_28|>",
2087
+ "<|reserved_special_token_29|>",
2088
+ "<|reserved_special_token_30|>",
2089
+ "<|reserved_special_token_31|>",
2090
+ "<|reserved_special_token_32|>",
2091
+ "<|reserved_special_token_33|>",
2092
+ "<|reserved_special_token_34|>",
2093
+ "<|reserved_special_token_35|>",
2094
+ "<|reserved_special_token_36|>",
2095
+ "<|reserved_special_token_37|>",
2096
+ "<|reserved_special_token_38|>",
2097
+ "<|reserved_special_token_39|>",
2098
+ "<|reserved_special_token_40|>",
2099
+ "<|reserved_special_token_41|>",
2100
+ "<|reserved_special_token_42|>",
2101
+ "<|reserved_special_token_43|>",
2102
+ "<|reserved_special_token_44|>",
2103
+ "<|reserved_special_token_45|>",
2104
+ "<|reserved_special_token_46|>",
2105
+ "<|reserved_special_token_47|>",
2106
+ "<|reserved_special_token_48|>",
2107
+ "<|reserved_special_token_49|>",
2108
+ "<|reserved_special_token_50|>",
2109
+ "<|reserved_special_token_51|>",
2110
+ "<|reserved_special_token_52|>",
2111
+ "<|reserved_special_token_53|>",
2112
+ "<|reserved_special_token_54|>",
2113
+ "<|reserved_special_token_55|>",
2114
+ "<|reserved_special_token_56|>",
2115
+ "<|reserved_special_token_57|>",
2116
+ "<|reserved_special_token_58|>",
2117
+ "<|reserved_special_token_59|>",
2118
+ "<|reserved_special_token_60|>",
2119
+ "<|reserved_special_token_61|>",
2120
+ "<|reserved_special_token_62|>",
2121
+ "<|reserved_special_token_63|>",
2122
+ "<|reserved_special_token_64|>",
2123
+ "<|reserved_special_token_65|>",
2124
+ "<|reserved_special_token_66|>",
2125
+ "<|reserved_special_token_67|>",
2126
+ "<|reserved_special_token_68|>",
2127
+ "<|reserved_special_token_69|>",
2128
+ "<|reserved_special_token_70|>",
2129
+ "<|reserved_special_token_71|>",
2130
+ "<|reserved_special_token_72|>",
2131
+ "<|reserved_special_token_73|>",
2132
+ "<|reserved_special_token_74|>",
2133
+ "<|reserved_special_token_75|>",
2134
+ "<|reserved_special_token_76|>",
2135
+ "<|reserved_special_token_77|>",
2136
+ "<|reserved_special_token_78|>",
2137
+ "<|reserved_special_token_79|>",
2138
+ "<|reserved_special_token_80|>",
2139
+ "<|reserved_special_token_81|>",
2140
+ "<|reserved_special_token_82|>",
2141
+ "<|reserved_special_token_83|>",
2142
+ "<|reserved_special_token_84|>",
2143
+ "<|reserved_special_token_85|>",
2144
+ "<|reserved_special_token_86|>",
2145
+ "<|reserved_special_token_87|>",
2146
+ "<|reserved_special_token_88|>",
2147
+ "<|reserved_special_token_89|>",
2148
+ "<|reserved_special_token_90|>",
2149
+ "<|reserved_special_token_91|>",
2150
+ "<|reserved_special_token_92|>",
2151
+ "<|reserved_special_token_93|>",
2152
+ "<|reserved_special_token_94|>",
2153
+ "<|reserved_special_token_95|>",
2154
+ "<|reserved_special_token_96|>",
2155
+ "<|reserved_special_token_97|>",
2156
+ "<|reserved_special_token_98|>",
2157
+ "<|reserved_special_token_99|>",
2158
+ "<|reserved_special_token_100|>",
2159
+ "<|reserved_special_token_101|>",
2160
+ "<|reserved_special_token_102|>",
2161
+ "<|reserved_special_token_103|>",
2162
+ "<|reserved_special_token_104|>",
2163
+ "<|reserved_special_token_105|>",
2164
+ "<|reserved_special_token_106|>",
2165
+ "<|reserved_special_token_107|>",
2166
+ "<|reserved_special_token_108|>",
2167
+ "<|reserved_special_token_109|>",
2168
+ "<|reserved_special_token_110|>",
2169
+ "<|reserved_special_token_111|>",
2170
+ "<|reserved_special_token_112|>",
2171
+ "<|reserved_special_token_113|>",
2172
+ "<|reserved_special_token_114|>",
2173
+ "<|reserved_special_token_115|>",
2174
+ "<|reserved_special_token_116|>",
2175
+ "<|reserved_special_token_117|>",
2176
+ "<|reserved_special_token_118|>",
2177
+ "<|reserved_special_token_119|>",
2178
+ "<|reserved_special_token_120|>",
2179
+ "<|reserved_special_token_121|>",
2180
+ "<|reserved_special_token_122|>",
2181
+ "<|reserved_special_token_123|>",
2182
+ "<|reserved_special_token_124|>",
2183
+ "<|reserved_special_token_125|>",
2184
+ "<|reserved_special_token_126|>",
2185
+ "<|reserved_special_token_127|>",
2186
+ "<|reserved_special_token_128|>",
2187
+ "<|reserved_special_token_129|>",
2188
+ "<|reserved_special_token_130|>",
2189
+ "<|reserved_special_token_131|>",
2190
+ "<|reserved_special_token_132|>",
2191
+ "<|reserved_special_token_133|>",
2192
+ "<|reserved_special_token_134|>",
2193
+ "<|reserved_special_token_135|>",
2194
+ "<|reserved_special_token_136|>",
2195
+ "<|reserved_special_token_137|>",
2196
+ "<|reserved_special_token_138|>",
2197
+ "<|reserved_special_token_139|>",
2198
+ "<|reserved_special_token_140|>",
2199
+ "<|reserved_special_token_141|>",
2200
+ "<|reserved_special_token_142|>",
2201
+ "<|reserved_special_token_143|>",
2202
+ "<|reserved_special_token_144|>",
2203
+ "<|reserved_special_token_145|>",
2204
+ "<|reserved_special_token_146|>",
2205
+ "<|reserved_special_token_147|>",
2206
+ "<|reserved_special_token_148|>",
2207
+ "<|reserved_special_token_149|>",
2208
+ "<|reserved_special_token_150|>",
2209
+ "<|reserved_special_token_151|>",
2210
+ "<|reserved_special_token_152|>",
2211
+ "<|reserved_special_token_153|>",
2212
+ "<|reserved_special_token_154|>",
2213
+ "<|reserved_special_token_155|>",
2214
+ "<|reserved_special_token_156|>",
2215
+ "<|reserved_special_token_157|>",
2216
+ "<|reserved_special_token_158|>",
2217
+ "<|reserved_special_token_159|>",
2218
+ "<|reserved_special_token_160|>",
2219
+ "<|reserved_special_token_161|>",
2220
+ "<|reserved_special_token_162|>",
2221
+ "<|reserved_special_token_163|>",
2222
+ "<|reserved_special_token_164|>",
2223
+ "<|reserved_special_token_165|>",
2224
+ "<|reserved_special_token_166|>",
2225
+ "<|reserved_special_token_167|>",
2226
+ "<|reserved_special_token_168|>",
2227
+ "<|reserved_special_token_169|>",
2228
+ "<|reserved_special_token_170|>",
2229
+ "<|reserved_special_token_171|>",
2230
+ "<|reserved_special_token_172|>",
2231
+ "<|reserved_special_token_173|>",
2232
+ "<|reserved_special_token_174|>",
2233
+ "<|reserved_special_token_175|>",
2234
+ "<|reserved_special_token_176|>",
2235
+ "<|reserved_special_token_177|>",
2236
+ "<|reserved_special_token_178|>",
2237
+ "<|reserved_special_token_179|>",
2238
+ "<|reserved_special_token_180|>",
2239
+ "<|reserved_special_token_181|>",
2240
+ "<|reserved_special_token_182|>",
2241
+ "<|reserved_special_token_183|>",
2242
+ "<|reserved_special_token_184|>",
2243
+ "<|reserved_special_token_185|>",
2244
+ "<|reserved_special_token_186|>",
2245
+ "<|reserved_special_token_187|>",
2246
+ "<|reserved_special_token_188|>",
2247
+ "<|reserved_special_token_189|>",
2248
+ "<|reserved_special_token_190|>",
2249
+ "<|reserved_special_token_191|>",
2250
+ "<|reserved_special_token_192|>",
2251
+ "<|reserved_special_token_193|>",
2252
+ "<|reserved_special_token_194|>",
2253
+ "<|reserved_special_token_195|>",
2254
+ "<|reserved_special_token_196|>",
2255
+ "<|reserved_special_token_197|>",
2256
+ "<|reserved_special_token_198|>",
2257
+ "<|reserved_special_token_199|>",
2258
+ "<|reserved_special_token_200|>",
2259
+ "<|reserved_special_token_201|>",
2260
+ "<|reserved_special_token_202|>",
2261
+ "<|reserved_special_token_203|>",
2262
+ "<|reserved_special_token_204|>",
2263
+ "<|reserved_special_token_205|>",
2264
+ "<|reserved_special_token_206|>",
2265
+ "<|reserved_special_token_207|>",
2266
+ "<|reserved_special_token_208|>",
2267
+ "<|reserved_special_token_209|>",
2268
+ "<|reserved_special_token_210|>",
2269
+ "<|reserved_special_token_211|>",
2270
+ "<|reserved_special_token_212|>",
2271
+ "<|reserved_special_token_213|>",
2272
+ "<|reserved_special_token_214|>",
2273
+ "<|reserved_special_token_215|>",
2274
+ "<|reserved_special_token_216|>",
2275
+ "<|reserved_special_token_217|>",
2276
+ "<|reserved_special_token_218|>",
2277
+ "<|reserved_special_token_219|>",
2278
+ "<|reserved_special_token_220|>",
2279
+ "<|reserved_special_token_221|>",
2280
+ "<|reserved_special_token_222|>",
2281
+ "<|reserved_special_token_223|>",
2282
+ "<|reserved_special_token_224|>",
2283
+ "<|reserved_special_token_225|>",
2284
+ "<|reserved_special_token_226|>",
2285
+ "<|reserved_special_token_227|>",
2286
+ "<|reserved_special_token_228|>",
2287
+ "<|reserved_special_token_229|>",
2288
+ "<|reserved_special_token_230|>",
2289
+ "<|reserved_special_token_231|>",
2290
+ "<|reserved_special_token_232|>",
2291
+ "<|reserved_special_token_233|>",
2292
+ "<|reserved_special_token_234|>",
2293
+ "<|reserved_special_token_235|>",
2294
+ "<|reserved_special_token_236|>",
2295
+ "<|reserved_special_token_237|>",
2296
+ "<|reserved_special_token_238|>",
2297
+ "<|reserved_special_token_239|>",
2298
+ "<|reserved_special_token_240|>",
2299
+ "<|reserved_special_token_241|>",
2300
+ "<|reserved_special_token_242|>",
2301
+ "<|reserved_special_token_243|>",
2302
+ "<|reserved_special_token_244|>",
2303
+ "<|reserved_special_token_245|>",
2304
+ "<|reserved_special_token_246|>",
2305
+ "<|reserved_special_token_247|>",
2306
+ "<|reserved_special_token_248|>",
2307
+ "<|reserved_special_token_249|>",
2308
+ "<|reserved_special_token_250|>"
2309
+ ],
2310
+ "bos_token": "<|begin_of_text|>",
2311
+ "chat_template": "{% set loop_messages = messages %}{% for message in loop_messages %}{% set content = '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' %}{% if loop.index0 == 0 %}{% set content = bos_token + content %}{% endif %}{{ content }}{% endfor %}{{ '<|start_header_id|>assistant<|end_header_id|>\n\n' }}",
2312
+ "clean_up_tokenization_spaces": true,
2313
+ "eos_token": "<|end_of_text|>",
2314
+ "model_input_names": [
2315
+ "input_ids",
2316
+ "attention_mask"
2317
+ ],
2318
+ "model_max_length": 3072,
2319
+ "padding_side": "right",
2320
+ "tokenizer_class": "PreTrainedTokenizerFast",
2321
+ "unk_token": "<|reserved_special_token_250|>"
2322
+ }