Create README.md

README.md

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+---
+language:
+- en
+pipeline_tag: text2text-generation
+inference: false
+---
+# ViPE-S-CTX7
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+ViPE: Visualize Pretty-much Everything, is the first automated model for translating any arbitrary piece of text into a visualizable prompt. It helps any text-to-image model in figurative or non-lexical language visualizations.
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+- **Developed by:** [Computer Graphics Group, University of Tuebingen](https://uni-tuebingen.de/fakultaeten/mathematisch-naturwissenschaftliche-fakultaet/fachbereiche/informatik/lehrstuehle/computergrafik/lehrstuhl/)
+- **Model type:** Auto-Regressive
+- **Language:** English
+- **License:** [MIT License for Non-Commercial Use](https://github.com/Hazel1994/ViPE/blob/main/LICENSE)
+- **Based on:** [GPT2-Small](https://huggingface.co/gpt2)
+- **Versions:** [ViPE-M-CTX7](https://huggingface.co/fittar/ViPE-M-CTX7) (255M parameters) and [ViPE-S-CTX7](https://huggingface.co/fittar/ViPE-S-CTX7) (117M parameters), 
+
+
+### Model Sources
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [Github](https://github.com/Hazel1994/ViPE)
+- **Paper:** [EMNLP2023](https://2023.emnlp.org/program/)
+
+### Down Stream Applications
+ViPE provides a robust backbone for many practical applications such as music video generation and creative writing.
+- #### Music Video Genrations
+  - **Repository:** [Github](https://github.com/Hazel1994/ViPE)
+  - **Demo:** [ViPE Videos](youtube link)
+- #### Creative Writing
+  - **Demo:** [Hugging Face Playground](https://huggingface.co/spaces/fittar/ViPE)
+
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+You can directly use the model to generate detailed prompts for any arbitrary text.
+
+
+```python
+from transformers import GPT2LMHeadModel, GPT2Tokenizer
+
+
+def generate(text, model, tokenizer,device,do_sample,top_k=100, epsilon_cutoff=.00005, temperature=1):
+    #mark the text with special tokens
+    text=[tokenizer.eos_token +  i + tokenizer.eos_token for i in text]
+    batch=tokenizer(text, padding=True, return_tensors="pt")
+
+    input_ids = batch["input_ids"].to(device)
+    attention_mask = batch["attention_mask"].to(device)
+
+    #how many new tokens to generate at max
+    max_prompt_length=50
+
+    generated_ids = model.generate(input_ids=input_ids,attention_mask=attention_mask, max_new_tokens=max_prompt_length, do_sample=do_sample,top_k=top_k, epsilon_cutoff=epsilon_cutoff, temperature=temperature)
+    #return only the generated prompts
+    pred_caps = tokenizer.batch_decode(generated_ids[:, -(generated_ids.shape[1] - input_ids.shape[1]):], skip_special_tokens=True)
+
+    return pred_caps
+
+device='cpu'
+model = GPT2LMHeadModel.from_pretrained('fittar/ViPE-S-CTX7')
+model.to(device)
+
+#ViPE-M's tokenizer is identical to that of GPT2-Medium
+tokenizer = GPT2Tokenizer.from_pretrained('gpt2-medium')
+tokenizer.pad_token = tokenizer.eos_token
+
+# A list of abstract/figurative or any arbitrary combinations of keywords
+texts=['lalala', 'I wanna start learning', 'free your mind; you will see the other side of life', 'brave; fantasy']
+
+prompts=generate(texts,model,tokenizer,do_sample=True,device=device)
+for t,p in zip(texts,prompts):
+    print('{} --> {}'.format(t,p))
+
+
+```
+
+
+### Recommendations
+
+You can use either a comma or a semicolon to combine multiple keywords. for example ['dark, fantasy, brave'] or  ['This is gonna be the best day of my life; do you agree?'].
+However, a semicolon draws a stronger boundary between the keywords and encourages the model to transfer the last keyword in a given context (previous keywords).
+
+
+## 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. -->
+
+[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. -->
+
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+
+## Citation
+
+If you find ViPE useful, please cite our paper.
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+
+## Model Card Contact
+
+[Hassan Shahmohammadi](https://fittar.me/)