Upload CustomViltForVQA
Browse files- README.md +199 -0
- config.json +249 -0
- model.safetensors +3 -0
README.md
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---
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library_name: transformers
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tags: []
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---
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# Model Card for Model ID
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<!-- Provide a quick summary of what the model is/does. -->
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## Model Details
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### Model Description
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<!-- Provide a longer summary of what this model is. -->
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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- **Developed by:** [More Information Needed]
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- **Funded by [optional]:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
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- **Model type:** [More Information Needed]
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- **Language(s) (NLP):** [More Information Needed]
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- **License:** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
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### Model Sources [optional]
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<!-- Provide the basic links for the model. -->
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- **Repository:** [More Information Needed]
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- **Paper [optional]:** [More Information Needed]
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- **Demo [optional]:** [More Information Needed]
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## Uses
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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### Direct Use
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<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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[More Information Needed]
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### Downstream Use [optional]
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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[More Information Needed]
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### Out-of-Scope Use
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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[More Information Needed]
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## Bias, Risks, and Limitations
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<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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[More Information Needed]
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### Recommendations
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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## How to Get Started with the Model
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Use the code below to get started with the model.
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[More Information Needed]
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## Training Details
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### Training Data
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<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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[More Information Needed]
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### Training Procedure
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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#### Preprocessing [optional]
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[More Information Needed]
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#### Training Hyperparameters
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- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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#### Speeds, Sizes, Times [optional]
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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[More Information Needed]
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## Evaluation
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<!-- This section describes the evaluation protocols and provides the results. -->
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### Testing Data, Factors & Metrics
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#### Testing Data
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<!-- This should link to a Dataset Card if possible. -->
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[More Information Needed]
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#### Factors
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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[More Information Needed]
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#### Metrics
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<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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[More Information Needed]
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### Results
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[More Information Needed]
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#### Summary
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## Model Examination [optional]
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<!-- Relevant interpretability work for the model goes here -->
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[More Information Needed]
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## Environmental Impact
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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- **Hardware Type:** [More Information Needed]
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- **Hours used:** [More Information Needed]
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- **Cloud Provider:** [More Information Needed]
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- **Compute Region:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
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## Technical Specifications [optional]
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### Model Architecture and Objective
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[More Information Needed]
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### Compute Infrastructure
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[More Information Needed]
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#### Hardware
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[More Information Needed]
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#### Software
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[More Information Needed]
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## Citation [optional]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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**BibTeX:**
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[More Information Needed]
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**APA:**
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[More Information Needed]
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## Glossary [optional]
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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[More Information Needed]
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## More Information [optional]
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[More Information Needed]
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## Model Card Authors [optional]
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[More Information Needed]
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## Model Card Contact
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[More Information Needed]
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config.json
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{
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"_name_or_path": "dandelin/vilt-b32-finetuned-vqa",
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"architectures": [
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"CustomViltForVQA"
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],
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"attention_probs_dropout_prob": 0.0,
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"hidden_act": "gelu",
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"hidden_dropout_prob": 0.0,
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"hidden_size": 768,
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"id2label": {
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"0": "sandwich",
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"1": "6",
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"2": "vase",
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"3": "5",
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"4": "truck",
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"5": "carrot",
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"6": "potted plant",
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"7": "car",
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"8": "apple",
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"9": "donut",
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"10": "fork",
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"11": "handbag",
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"12": "tv",
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"13": "motorcycle",
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"14": "baseball glove",
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"15": "surfboard",
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"16": "toilet",
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"17": "person",
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"18": "bottle",
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"19": "bear",
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"20": "dining table",
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"21": "knife",
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"22": "cell phone",
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"23": "No",
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"24": "Blue",
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"25": "Green",
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"26": "bowl",
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"27": "spoon",
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"28": "broccoli",
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"29": "0",
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"30": "toothbrush",
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"31": "sheep",
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"32": "elephant",
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"33": "giraffe",
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"34": "cake",
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"35": "parking meter",
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"36": "cat",
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"37": "traffic light",
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"38": "Behind",
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"39": "Yes",
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"40": "wine glass",
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"41": "Right",
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"42": "Brown",
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"43": "stop sign",
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"44": "right",
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"45": "sink",
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"46": "sports ball",
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"47": "book",
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"48": "horse",
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"49": "7",
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"50": "bench",
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"51": "tie",
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"52": "cup",
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"53": "fire hydrant",
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"54": "hot dog",
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"55": "skateboard",
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"56": "Orange",
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"57": "remote",
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"58": "baseball bat",
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"59": "oven",
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"60": "train",
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"61": "Below",
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"62": "8",
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"63": "Red",
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"64": "mouse",
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"65": "teddy bear",
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"66": "chair",
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"67": "keyboard",
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"68": "2",
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"69": "zebra",
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"70": "laptop",
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"71": "Front",
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"72": "1",
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"73": "clock",
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"74": "Grey",
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"75": "pizza",
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"76": "umbrella",
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"77": "above",
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"78": "kite",
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"79": "bus",
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"80": "Black",
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"81": "Yellow",
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"82": "frisbee",
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"83": "airplane",
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"84": "White",
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"85": "left",
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"86": "bird",
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"87": "Pink",
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"88": "orange",
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"89": "bicycle",
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"90": "couch",
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"91": "Purple",
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"92": "skis",
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"93": "3",
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"94": "9",
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"95": "refrigerator",
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"96": "snowboard",
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"97": "scissors",
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"98": "tennis racket",
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110 |
+
"99": "suitcase",
|
111 |
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"100": "cow",
|
112 |
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"101": "bed",
|
113 |
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"102": "dog",
|
114 |
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"103": "boat",
|
115 |
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"104": "4",
|
116 |
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"105": "Above",
|
117 |
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"106": "banana",
|
118 |
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"107": "Left"
|
119 |
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},
|
120 |
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
134 |
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|
135 |
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|
136 |
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"Below": 61,
|
137 |
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|
138 |
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|
139 |
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|
140 |
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|
141 |
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|
142 |
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|
143 |
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|
144 |
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|
145 |
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|
146 |
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|
147 |
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|
148 |
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|
149 |
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|
150 |
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|
151 |
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|
152 |
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|
153 |
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|
154 |
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|
155 |
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|
156 |
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|
157 |
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|
158 |
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|
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|
160 |
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|
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"bench": 50,
|
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|
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"bird": 86,
|
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|
165 |
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|
166 |
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|
167 |
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|
168 |
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|
169 |
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|
170 |
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|
171 |
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|
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|
173 |
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|
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|
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|
176 |
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|
177 |
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|
178 |
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|
179 |
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|
180 |
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|
181 |
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|
182 |
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|
183 |
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"elephant": 32,
|
184 |
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|
185 |
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"fork": 10,
|
186 |
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|
187 |
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|
188 |
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|
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"horse": 48,
|
190 |
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|
191 |
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"keyboard": 67,
|
192 |
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|
193 |
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|
194 |
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|
195 |
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"left": 85,
|
196 |
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"motorcycle": 13,
|
197 |
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"mouse": 64,
|
198 |
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"orange": 88,
|
199 |
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"oven": 59,
|
200 |
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"parking meter": 35,
|
201 |
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"person": 17,
|
202 |
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"pizza": 75,
|
203 |
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"potted plant": 6,
|
204 |
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"refrigerator": 95,
|
205 |
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"remote": 57,
|
206 |
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|
207 |
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|
208 |
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"scissors": 97,
|
209 |
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"sheep": 31,
|
210 |
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"sink": 45,
|
211 |
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"skateboard": 55,
|
212 |
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"skis": 92,
|
213 |
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"snowboard": 96,
|
214 |
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"spoon": 27,
|
215 |
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"sports ball": 46,
|
216 |
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"stop sign": 43,
|
217 |
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"suitcase": 99,
|
218 |
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"surfboard": 15,
|
219 |
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"teddy bear": 65,
|
220 |
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"tennis racket": 98,
|
221 |
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"tie": 51,
|
222 |
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"toilet": 16,
|
223 |
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"toothbrush": 30,
|
224 |
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"traffic light": 37,
|
225 |
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"train": 60,
|
226 |
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"truck": 4,
|
227 |
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"tv": 12,
|
228 |
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"umbrella": 76,
|
229 |
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"vase": 2,
|
230 |
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"wine glass": 40,
|
231 |
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"zebra": 69
|
232 |
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},
|
233 |
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"layer_norm_eps": 1e-12,
|
234 |
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"max_image_length": -1,
|
235 |
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"max_position_embeddings": 40,
|
236 |
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"modality_type_vocab_size": 2,
|
237 |
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"model_type": "vilt",
|
238 |
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"num_attention_heads": 12,
|
239 |
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"num_channels": 3,
|
240 |
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"num_hidden_layers": 12,
|
241 |
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"num_images": -1,
|
242 |
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"patch_size": 32,
|
243 |
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"qkv_bias": true,
|
244 |
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"tie_word_embeddings": false,
|
245 |
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"torch_dtype": "float32",
|
246 |
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"transformers_version": "4.49.0",
|
247 |
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"type_vocab_size": 2,
|
248 |
+
"vocab_size": 30522
|
249 |
+
}
|
model.safetensors
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:bf5db5df72eaae9c0eb762a0b4cbd0aaa11a77fee2c8c757b4ea25c8589ae8eb
|
3 |
+
size 446736704
|