Krudev
/

TubeCLIP

@@ -1,197 +1,81 @@
----
-pipeline_tag: zero-shot-classification
----
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
-This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
-## Model Details
-### Model Description
-<!-- Provide a longer summary of what this model is. -->
-- **Developed by:**
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
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-- **Finetuned from model [optional]:** [More Information Needed]
-### Model Sources [optional]
-<!-- Provide the basic links for the model. -->
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-- **Demo [optional]:** [More Information Needed]
-## Uses
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-### Direct Use
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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-### Downstream Use [optional]
-<!-- 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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-### Out-of-Scope Use
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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-## Bias, Risks, and Limitations
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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-### Recommendations
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-## How to Get Started with the Model
-Use the code below to get started with the model.
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-## Training Details
-### Training Data
-<!-- 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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-### Training Procedure
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-#### Preprocessing [optional]
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-#### Training Hyperparameters
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-#### Speeds, Sizes, Times [optional]
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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-## Evaluation
-<!-- This section describes the evaluation protocols and provides the results. -->
-### Testing Data, Factors & Metrics
-#### Testing Data
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-#### Factors
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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-#### Metrics
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-### Results
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-#### Summary
-## Model Examination [optional]
-<!-- Relevant interpretability work for the model goes here -->
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-## Environmental Impact
-<!-- 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 Card Authors [optional]
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-## Model Card Contact
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+### 1. Model Card Template (`README.md` for Hugging Face)
+Create a `README.md` file in your Hugging Face model repository and paste this:
+```markdown
+---
+language:
+- en
+license: mit
+tags:
+- clip
+- vision
+- multimodal
+- youtube
+- thumbnail-analysis
+pipeline_tag: feature-extraction
+---
+# Tube Clip (Fine-Tuned CLIP Large)
+Tube Clip is a fine-tuned variant of CLIP Large (~1.7 GB) designed to analyze YouTube video thumbnails and titles to predict view performance classes.
+## Model Overview
+* **Architecture:** CLIP (Vision + Text Transformer) fine-tuned on YouTube thumbnail and title distributions.
+* **Input:** Video thumbnail image + video title string.
+* **Output:** Predicted view tier / performance classification.
+* **Weight Size:** ~1.7 GB
+## Usage
+You can download the model weights directly or load them via the Hugging Face Hub:
+```python
+from huggingface_hub import hf_hub_download
+import torch
+# Download weights
+model_path = hf_hub_download(
+    repo_id="your-username/tube-clip",
+    filename="pytorch_model.bin"  # replace with your actual file name
+)
+# Load into your custom architecture
+# model.load_state_dict(torch.load(model_path, map_location="cpu"))
+```
+## Associated Research & Analysis
+This model was evaluated on a 100,000-video stratified evaluation pipeline. For data pipelines, reproduction scripts, and methodology, visit the main GitHub repository:
+* **GitHub Repository:** [100k-thumbnail-analysis](https://github.com/krishnendu1233/100k-thumbnail-analysis)
+```
+---
+### 2. How Users Can Download It
+Once your weights (e.g., `pytorch_model.bin`, `model.safetensors`, or `tube_clip.pt`) are uploaded to the repo, users can download the model in three standard ways:
+1. **Direct Web Download:**
+   Anyone can click on the **Files and versions** tab in your Hugging Face repository, click the file, and press the **download** button.
+2. **Via Python (`huggingface_hub`):**
+   Users can fetch the weights programmatically in one line:
+   ```python
+   from huggingface_hub import hf_hub_download
+   file_path = hf_hub_download(repo_id="your-username/tube-clip", filename="model.safetensors")
+```
+3. **Via Git LFS:**
+Users can clone the entire repository and weights using terminal:
+```bash
+git lfs install
+git clone https://huggingface.co/your-username/tube-clip
+```