Update README.md

README.md

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 ---
-# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1
-# Doc / guide: https://huggingface.co/docs/hub/model-cards
-{}
+tags:
+- vision
+- clip
+- clip4clip
+- video
 ---
 
-# Model Card for Model ID
+# Model Card
+## Details
 
-This is a trained Clip4Clip model to search videos from a text sentence
+This model was trained via CLIP4Clip (a CLIP-based a CLIP-based video retrival method, based on this [paper](https://arxiv.org/pdf/2104.08860.pdf) and [code](https://github.com/ArrowLuo/CLIP4Clip).
+This model was trained on 150k videos from the [WebVid Dataset](https://m-bain.github.io/webvid-dataset/) (a large-scale dataset of short videos with textual descriptions sourced from the web).
 
-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).
+We adjucted the weights of the clip model we achieved from our training to the model implameted in [clip-vit-base-patch32](https://huggingface.co/openai/clip-vit-base-patch32) and added few changes for the last layers.
 
-## Model Details
 
-### Model Description
-
-<!-- Provide a longer summary of what this model is. -->
-
-
-
-- **Developed by:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-
-## Use
-To encode text:
+### Use with Transformers
 
 ```python
-import torch
 import numpy as np
+import torch
 from transformers import AutoTokenizer, CLIPTextModelWithProjection
 
-search_sentence = "a woman drinking coffee looking at the sea"
 
-my_model = CLIPTextModelWithProjection.from_pretrained("Diangle/clip4clip-webvid")
+search_sentence = "a basketball player performing a slam dunk"
+
+model = CLIPTextModelWithProjection.from_pretrained("Diangle/clip4clip-webvid")
 tokenizer = AutoTokenizer.from_pretrained("Diangle/clip4clip-webvid")
 
 
 inputs = tokenizer(text=search_sentence , return_tensors="pt", padding=True)
 
-outputs = my_model(input_ids=inputs["input_ids"], attention_mask=inputs["attention_mask"], return_dict=False)
-       
-text_projection = my_model.state_dict()['text_projection.weight']
+outputs = model(input_ids=inputs["input_ids"], attention_mask=inputs["attention_mask"], return_dict=False)
+
+# Adding special projection and changing last layers:      
+text_projection = model.state_dict()['text_projection.weight']
 text_embeds = outputs[1] @ text_projection
 final_output = text_embeds[torch.arange(text_embeds.shape[0]), inputs["input_ids"].argmax(dim=-1)]
 
-
+# Normalizing the embeddings:
 final_output = final_output / final_output.norm(dim=-1, keepdim=True)
 final_output = final_output.cpu().detach().numpy()
 sequence_output = final_output / np.sum(final_output**2, axis=1, keepdims=True)
-print(sequence_output)
+print("sequence_output: ", sequence_output)
 ```
 
 
-### Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
-
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### 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.
-
-[More Information Needed]
-
-## Training Details
-
-### Training Data
-
-<!-- This should link to a Data 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. -->
-
-[More Information Needed]
-
-### 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]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Data Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## 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).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## Citation [optional]
+## Model Use
 
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+### Intended Use
 
-**BibTeX:**
+This model is intended to use for video retrival, look for example **this space**. 
 
-[More Information Needed]
+### Extra Information
 
-**APA:**
+For video embedding there is an extra notebook that describes how to embedd videos.
 
-[More Information Needed]
 
-## Glossary [optional]
 
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+## Performance and Limitations
 
-[More Information Needed]
+### Performance
 
-## More Information [optional]
+We have evaluated the performance 
 
-[More Information Needed]
 
-## Model Card Authors [optional]
 
-[More Information Needed]
+## Limitations
 
-## Model Card Contact
 
-[More Information Needed]
+## Feedback
 
+### Where to send questions or comments about the model
 
+Please use [this Google Form](https://forms.gle/Uv7afRH5dvY34ZEs9)