Update README.md

README.md

@@ -30,45 +30,64 @@ ViPE: Visualize Pretty-much Everything, is the first automated model for transla
 - **Paper:** [EMNLP2023](https://2023.emnlp.org/program/)
 - **Demo:**[ViPE Videos] (youtube link)
 
-## 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. -->
 
-[More Information Needed]
+you can directly use the model to generate detailed prompts for any arbitrary text.
 
-### Downstream Use [optional]
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+```python
+from transformers import GPT2LMHeadModel, GPT2Tokenizer
 
-[More Information Needed]
 
-### Out-of-Scope Use
+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")
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+    input_ids = batch["input_ids"].to(device)
+    attention_mask = batch["attention_mask"].to(device)
 
-[More Information Needed]
+    #how many new tokens to generate at max
+    max_prompt_length=50
 
-## Bias, Risks, and Limitations
+    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)
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+    return pred_caps
 
-[More Information Needed]
+device='cpu'
+model = GPT2LMHeadModel.from_pretrained('fittar/ViPE-M-CTX7')
+model.to(device)
 
-### Recommendations
+#ViPE-M's tokenizer is identical to that of GPT2-Medium
+tokenizer = GPT2Tokenizer.from_pretrained('gpt2-medium')
+tokenizer.pad_token = tokenizer.eos_token
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+# 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']
 
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+prompts=generate(texts,model,tokenizer,do_sample=True,device=device)
+for t,p in zip(texts,prompts):
+    print('{} --> {}'.format(t,p))
 
-## How to Get Started with the Model
+lalala -->  A group of people chanting "la la la" around a bonfire on a beach at night
+I wanna start learning -->  A child sitting in a library surrounded by books, excitedly flipping through pages of a book
+free your mind; you will see the other side of life -->  An astronaut floating in space with a sense of floating weightlessness, looking down towards the earth
+brave; fantasy -->  A brave knight with shining armor fighting a fierce dragon in a misty forest
+
+```
+
+
+### Recommendations
+
+For combining multiple keywords, separate them using a comma. for example ['dark, fantasy, brave']
+for phrases or sentences, a semicolon is preferable. For example ['This is gonna be the best day of my life; do you agree?']
 
-Use the code below to get started with the model.
 
-[More Information Needed]
 
 ## Training Details