Update README.md

README.md

@@ -3,197 +3,229 @@ library_name: transformers
 tags: []
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-# Model Card for Model ID
+# SUMMARY
 
-<!-- Provide a quick summary of what the model is/does. -->
-
-
-
-## Model Details
-
-### Model Description
-
-<!-- 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]
-- **Funded by [optional]:** [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]
+Just a model using to learn Fine Tuning of 'DialoGPT-medium'
+- on a self made datasets
+- on a self made special tokens
+- on a multiple fine tuned with ~30K dataset (in progress mode)
 
-## 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]
+If interested in how I got to this point and how I created the datasets you can visit:  
+[Crafting GPT2 for Personalized AI-Preparing Data the Long Way](https://medium.com/@deeokay/the-soul-in-the-machine-crafting-gpt2-for-personalized-ai-9d38be3f635f)
+<!-- Provide a quick summary of what the model is/does. -->
 
-### Downstream Use [optional]
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+## DECLARING NEW SPECIAL TOKENS 
+
+```python
+special_tokens_dict = {
+    'eos_token': '<|STOP|>',
+    'bos_token': '<|STOP|>',
+    'pad_token': '<|PAD|>',
+    'additional_special_tokens': ['<|BEGIN_QUERY|>', '<|BEGIN_QUERY|>', 
+                                  '<|BEGIN_ANALYSIS|>', '<|END_ANALYSIS|>',
+                                  '<|BEGIN_RESPONSE|>', '<|END_RESPONSE|>',
+                                  '<|BEGIN_SENTIMENT|>', '<|END_SENTIMENT|>',
+                                  '<|BEGIN_CLASSIFICATION|>', '<|END_CLASSIFICATION|>',]
+}
+
+tokenizer.add_special_tokens(special_tokens_dict)
+model.resize_token_embeddings(len(tokenizer))
+
+tokenizer.eos_token_id = tokenizer.convert_tokens_to_ids('<|STOP|>')
+tokenizer.bos_token_id = tokenizer.convert_tokens_to_ids('<|STOP|>')
+tokenizer.pad_token_id = tokenizer.convert_tokens_to_ids('<|PAD|>')
+```
+
+The order of tokens is as follows: 
+
+```python
+def combine_text(user_prompt, analysis, sentiment, new_response, classification):
+    user_q = f"<|STOP|><|BEGIN_QUERY|>{user_prompt}<|END_QUERY|>"
+    analysis = f"<|BEGIN_ANALYSIS|>{analysis}<|END_ANALYSIS|>"
+    new_response = f"<|BEGIN_RESPONSE|>{new_response}<|END_RESPONSE|>"
+    sentiment = f"<|BEGIN_SENTIMENT|>Sentiment: {sentiment}<|END_SENTIMENT|><|STOP|>"
+    classification = f"<|BEGIN_CLASSIFICATION|>{classification}<|END_CLASSIFICATION|>"
+    return user_q + analysis + new_response + classification + sentiment
+```
+
+## INFERANCING 
+
+I am currently testing two ways, if anyone knows a better one, please let me know! 
+
+```python
+import torch
+from transformers import AutoModelForCausalLLM, AutoTokenizer
+
+models_folder = "Deeokay/DialoGPT-special-tokens-medium4"
+
+model = AutoModelForCausalLM.from_pretrained(models_folder)
+tokenizer = AutoTokenizer.from_pretrained(models_folder)
+
+# Device configuration <<change as needed>> 
+device = torch.device("cpu")
+model.to(device)
+
+```
+
+### OPTION 1 INFERFENCE
+
+```python
+import time
+
+class Stopwatch:
+    def __init__(self):
+        self.start_time = None
+        self.end_time = None
+
+    def start(self):
+        self.start_time = time.time()
+
+    def stop(self):
+        self.end_time = time.time()
+
+    def elapsed_time(self):
+        if self.start_time is None:
+            return "Stopwatch hasn't been started"
+        if self.end_time is None:
+            return "Stopwatch hasn't been stopped"
+        return self.end_time - self.start_time
+
+stopwatch1 = Stopwatch()
+
+def generate_response(input_text, max_length=250):
+    
+	stopwatch1.start()
+    
+	# Prepare the input
+	# input_text = f"<|BEGIN_QUERY|>{input_text}<|END_QUERY|><|BEGIN_ANALYSIS|>{input_text}<|END_ANALYSIS|><|BEGIN_RESPONSE|>"
+	input_text = f"<|BEGIN_QUERY|>{input_text}<|END_QUERY|><|BEGIN_ANALYSIS|>"
+    
+	input_ids = tokenizer.encode(input_text, return_tensors="pt").to(device)
+
+	# Create attention mask
+	attention_mask = torch.ones_like(input_ids).to(device)
+    
+	# Generate
+	output = model.generate(
+    	input_ids,
+    	max_new_tokens=max_length,
+    	num_return_sequences=1,
+    	no_repeat_ngram_size=2,
+    	attention_mask=attention_mask,
+    	pad_token_id=tokenizer.eos_token_id,
+    	eos_token_id=tokenizer.convert_tokens_to_ids('<|STOP|>'),
+	)
+    
+	stopwatch1.stop()
+	return tokenizer.decode(output[0], skip_special_tokens=False)
+```
+
+### OPTION 2 INFERNCE
+
+```python
+import time
+
+class Stopwatch:
+    def __init__(self):
+        self.start_time = None
+        self.end_time = None
+
+    def start(self):
+        self.start_time = time.time()
+
+    def stop(self):
+        self.end_time = time.time()
+
+    def elapsed_time(self):
+        if self.start_time is None:
+            return "Stopwatch hasn't been started"
+        if self.end_time is None:
+            return "Stopwatch hasn't been stopped"
+        return self.end_time - self.start_time
+
+stopwatch2 = Stopwatch()
+
+def generate_response2(input_text, max_length=250):
+    
+	stopwatch2.start()
+    
+	# Prepare the input
+	# input_text = f"<|BEGIN_QUERY|>{input_text}<|END_QUERY|><|BEGIN_ANALYSIS|>{input_text}<|END_ANALYSIS|><|BEGIN_RESPONSE|>"
+	input_text = f"<|BEGIN_QUERY|>{input_text}<|END_QUERY|><|BEGIN_ANALYSIS|>"
+	input_ids = tokenizer.encode(input_text, return_tensors="pt").to(device)
+
+	# Create attention mask
+	attention_mask = torch.ones_like(input_ids).to(device)
+
+	# # 2ND OPTION FOR : Generate
+	output = model.generate(
+    	input_ids,
+    	max_new_tokens=max_length,
+    	attention_mask=attention_mask,
+    	do_sample=True,
+    	temperature=0.4,
+    	top_k=60,
+    	no_repeat_ngram_size=2,
+    	pad_token_id=tokenizer.pad_token_id,
+    	eos_token_id=tokenizer.eos_token_id,
+	)
+    
+	stopwatch2.stop()
+	return tokenizer.decode(output[0], skip_special_tokens=False)
+```
+### DECODING ANSWER
+
+When I need just the response
+
+```python
+def decode(text):
+	full_text = text
+    
+	# Extract the response part
+	start_token = "<|BEGIN_RESPONSE|>"
+	end_token = "<|END_RESPONSE|>"
+	start_idx = full_text.find(start_token)
+	end_idx = full_text.find(end_token)
+    
+	if start_idx != -1 and end_idx != -1:
+    	response = full_text[start_idx + len(start_token):end_idx].strip()
+	else:
+    	response = full_text.strip()
+    
+	return response
+```
+
+### MY SETUP
+
+I use the stopwatch to time the responses and I use both inference to see the difference 
+
+```python
+input_text = "Who is Steve Jobs and what was contribution?"
+response1_full = generate_response(input_text)
+#response1 = decode(response1_full)
+print(f"Input: {input_text}")
+print("=======================================")
+print(f"Response1: {response1_full}")
+elapsed1 = stopwatch1.elapsed_time()
+print(f"Process took {elapsed1:.4f} seconds")
+print("=======================================")
+response2_full = generate_response2(input_text)
+#response2 = decode(response2_full)
+print(f"Response2: {response2_full}")
+elapsed2 = stopwatch2.elapsed_time()
+print(f"Process took {elapsed2:.4f} seconds")
+print("=======================================")
+```
 
-[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 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. -->
-
-#### 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 Dataset 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]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
+Well everything that has a factual data.. trust at your own risk! 
 
-[More Information Needed]
+Never tested on mathamatical knowledge. 
 
-## Model Card Authors [optional]
+I quite enjoy how the response feels closer to what I had in mind.. 
 
-[More Information Needed]
 
-## Model Card Contact
 
-[More Information Needed]