Update README.md

README.md

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 ---
-library_name: transformers
-tags: []
+language:
+- en
 ---
-
-# Model Card for Model ID
-
-<!-- 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. -->
-
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-
-- **Developed by:** [More Information Needed]
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-### Model Sources [optional]
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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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-### Downstream Use [optional]
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-### Out-of-Scope Use
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-## Bias, Risks, and Limitations
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-### Recommendations
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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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-## Training Details
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-### Training Data
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-### Training Procedure
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-#### Preprocessing [optional]
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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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-## Evaluation
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-### Testing Data, Factors & Metrics
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-#### Testing Data
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-#### Factors
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-#### Metrics
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-### Results
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-#### Summary
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-## Model Examination [optional]
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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 -->
-
-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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-## Technical Specifications [optional]
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-### Model Architecture and Objective
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-### Compute Infrastructure
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-#### Hardware
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-#### Software
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-## Citation [optional]
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-**BibTeX:**
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-## Glossary [optional]
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-## More Information [optional]
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-## Model Card Authors [optional]
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-## Model Card Contact
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+<style>
+  .title-container {
+    display: flex;
+    flex-direction: column; /* Stack elements vertically */
+    justify-content: center;
+    align-items: center;
+  }
+  
+  .title {
+    font-size: 2em;
+    text-align: center;
+    color: #333;
+    font-family: 'Comic Sans MS', cursive; /* Use Comic Sans MS font */
+    text-transform: uppercase;
+    letter-spacing: 0.1em;
+    padding: 0.5em 0 0.2em;
+    background: transparent;
+  }
+  
+  .title span {
+    background: -webkit-linear-gradient(45deg, #6495ED, #4169E1); /* Blue gradient */
+    -webkit-background-clip: text;
+    -webkit-text-fill-color: transparent;
+  }
+
+  .subheading {
+    font-size: 1.5em; /* Adjust the size as needed */
+    text-align: center;
+    color: #555; /* Adjust the color as needed */
+    font-family: 'Comic Sans MS', cursive; /* Use Comic Sans MS font */
+  }
+
+  .authors {
+    font-size: 1em; /* Adjust the size as needed */
+    text-align: center;
+    color: #777; /* Adjust the color as needed */
+    font-family: 'Comic Sans MS', cursive; /* Use Comic Sans MS font */
+    padding-top: 1em;
+  }
+
+  .affil {
+    font-size: 1em; /* Adjust the size as needed */
+    text-align: center;
+    color: #777; /* Adjust the color as needed */
+    font-family: 'Comic Sans MS', cursive; /* Use Comic Sans MS font */
+  }
+
+</style>
+
+<div class="title-container">
+  <div class="title">
+    Ta<span>il</span>s Tell Ta<span>le</span>s
+  </div>
+  <div class="subheading">
+    Chapter-Wide Manga Transcriptions With Character Names
+  </div>
+  <div class="authors">
+    Ragav Sachdeva, Gyungin Shin and Andrew Zisserman
+  </div>
+  <div class="affil">
+    University of Oxford
+  </div>
+</div>
+
+# Usage
+```python
+from transformers import AutoModel
+import PIL
+import torch
+
+batch_of_images = [PIL.Image.open("image1.jpg"), PIL.Image.open("image2.jpg")]
+model = AutoModel.from_pretrained("ragavsachdeva/magiv2-crop-embedder", trust_remote_code=True).cuda().eval()
+with torch.no_grad():
+    embeddings = model(batch_of_images)
+
+print(embeddings.shape)
+```
+
+# License and Citation
+The provided model is available for unrestricted use in personal, research, non-commercial, and not-for-profit endeavors. For any other usage scenarios, kindly contact me via email, providing a detailed description of your requirements, to establish a tailored licensing arrangement.
+My contact information can be found on my website: ragavsachdeva [dot] github [dot] io
+
+```
+@misc{magiv2,
+      title={Tails Tell Tales: Chapter-Wide Manga Transcriptions with Character Names}, 
+      author={Ragav Sachdeva and Gyungin Shin and Andrew Zisserman},
+      year={2024},
+      eprint={2408.00298},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2408.00298}, 
+}
+```