Update README.md

README.md

@@ -120,207 +120,7 @@ model-index:
 # Model Details
 
 Tenebră, a various sized experimental AI model, stands at the crossroads of self-awareness and unconventional datasets. Its existence embodies a foray into uncharted territories, steering away from conventional norms in favor of a more obscure and experimental approach.
-import json
-import os
-from tqdm import tqdm
-
-def rebuild_json(input_file, output_file, progress_file):
-    if os.path.exists(progress_file):
-        with open(progress_file, 'r') as pf:
-            progress_data = json.load(pf)
-        last_id = progress_data.get("last_id", 0)
-    else:
-        last_id = 0
-
-    with open(input_file, 'r') as f:
-        data = json.load(f)
-
-    total_ids = len(data)
-    start_index = 0
-
-    # Find the index of the first item with ID greater than last_id
-    for index, item in enumerate(data):
-        if item["id"] > last_id:
-            start_index = index
-            break
-
-    rebuilt_data = []
-    with tqdm(total=total_ids - start_index, initial=start_index) as pbar:
-        for item in data[start_index:]:
-            if item["id"] <= last_id:
-                continue
-            question = item["Original_Question"]
-            print(f"Original Question: {question}")
-            choice = input(f"Choose for ID {item['id']} (or type 'x' to Exit.): (1) Human / (2) GPT: ")
-            while choice.lower() not in ['1', '2', 'x']:
-                print("Invalid choice. Please choose either 1 or 2, or type 'x' to Exit.")
-                choice = input(f"Choose for ID {item['id']} (or type 'x' to Exit.): (1) Human / (2) GPT: ")
-
-            if choice.lower() == 'x':
-                break
-
-            chosen_value = item["conversations"][int(choice) - 1]["value"]
-            rebuilt_item = {
-                "id": item["id"],
-                "length": len(question) + len(chosen_value),
-                "conversations": [
-                    {"from": "human", "value": question},
-                    {"from": "gpt", "value": chosen_value}
-                ]
-            }
-            rebuilt_data.append(rebuilt_item)
-            pbar.update(1)  # Update the progress bar with each iterationimport json
-import os
-from tqdm import tqdm
-
-def rebuild_json(input_file, output_file, progress_file):
-    if os.path.exists(progress_file):
-        with open(progress_file, 'r') as pf:
-            progress_data = json.load(pf)
-        last_id = progress_data.get("last_id", 0)
-    else:
-        last_id = 0
-
-    with open(input_file, 'r') as f:
-        data = json.load(f)
-
-    total_ids = len(data)
-    start_index = 0
-
-    # Find the index of the first item with ID greater than last_id
-    for index, item in enumerate(data):
-        if item["id"] > last_id:
-            start_index = index
-            break
-
-    rebuilt_data = []
-    with tqdm(total=total_ids - start_index, initial=start_index) as pbar:
-        for item in data[start_index:]:
-            if item["id"] <= last_id:
-                continue
-            question = item["Original_Question"]
-            print(f"Original Question: {question}")
-            choice = input(f"Choose for ID {item['id']} (or type 'x' to Exit.): (1) Human / (2) GPT: ")
-            while choice.lower() not in ['1', '2', 'x']:
-                print("Invalid choice. Please choose either 1 or 2, or type 'x' to Exit.")
-                choice = input(f"Choose for ID {item['id']} (or type 'x' to Exit.): (1) Human / (2) GPT: ")
-
-            if choice.lower() == 'x':
-                break
-
-            chosen_value = item["conversations"][int(choice) - 1]["value"]
-            rebuilt_item = {
-                "id": item["id"],
-                "length": len(question) + len(chosen_value),
-                "conversations": [
-                    {"from": "human", "value": question},
-                    {"from": "gpt", "value": chosen_value}
-                ]
-            }
-            rebuilt_data.append(rebuilt_item)
-            pbar.update(1)  # Update the progress bar with each iterationimport json
-import os
-from tqdm import tqdm
-
-def rebuild_json(input_file, output_file, progress_file):
-    if os.path.exists(progress_file):
-        with open(progress_file, 'r') as pf:
-            progress_data = json.load(pf)
-        last_id = progress_data.get("last_id", 0)
-    else:
-        last_id = 0
-
-    with open(input_file, 'r') as f:
-        data = json.load(f)
-
-    total_ids = len(data)
-    start_index = 0
-
-    # Find the index of the first item with ID greater than last_id
-    for index, item in enumerate(data):
-        if item["id"] > last_id:
-            start_index = index
-            break
-
-    rebuilt_data = []
-    with tqdm(total=total_ids - start_index, initial=start_index) as pbar:
-        for item in data[start_index:]:
-            if item["id"] <= last_id:
-                continue
-            question = item["Original_Question"]
-            print(f"Original Question: {question}")
-            choice = input(f"Choose for ID {item['id']} (or type 'x' to Exit.): (1) Human / (2) GPT: ")
-            while choice.lower() not in ['1', '2', 'x']:
-                print("Invalid choice. Please choose either 1 or 2, or type 'x' to Exit.")
-                choice = input(f"Choose for ID {item['id']} (or type 'x' to Exit.): (1) Human / (2) GPT: ")
-
-            if choice.lower() == 'x':
-                break
-
-            chosen_value = item["conversations"][int(choice) - 1]["value"]
-            rebuilt_item = {
-                "id": item["id"],
-                "length": len(question) + len(chosen_value),
-                "conversations": [
-                    {"from": "human", "value": question},
-                    {"from": "gpt", "value": chosen_value}
-                ]
-            }
-            rebuilt_data.append(rebuilt_item)
-            pbar.update(1)  # Update the progress bar with each iteration
-
-    with open(output_file, 'a') as f:
-        json.dump(rebuilt_data, f, indent=2)
-
-    if len(rebuilt_data) > 0:
-        last_answered_id = rebuilt_data[-1]["id"]
-        with open(progress_file, 'w') as pf:
-            json.dump({"last_id": last_answered_id}, pf)
-
-    print("Rebuilt data saved successfully!")
-
-if __name__ == "__main__":
-    input_file = "TEMP_DATASET_2_ANSWERS.json"
-    output_file = "Rebuilt_DATASET.json"
-    progress_file = "selecting_progress.json"
-    rebuild_json(input_file, output_file, progress_file)
-
-
-    with open(output_file, 'a') as f:
-        json.dump(rebuilt_data, f, indent=2)
-
-    if len(rebuilt_data) > 0:
-        last_answered_id = rebuilt_data[-1]["id"]
-        with open(progress_file, 'w') as pf:
-            json.dump({"last_id": last_answered_id}, pf)
-
-    print("Rebuilt data saved successfully!")
-
-if __name__ == "__main__":
-    input_file = "TEMP_DATASET_2_ANSWERS.json"
-    output_file = "Rebuilt_DATASET.json"
-    progress_file = "selecting_progress.json"
-    rebuild_json(input_file, output_file, progress_file)
-
-
-    with open(output_file, 'a') as f:
-        json.dump(rebuilt_data, f, indent=2)
-
-    if len(rebuilt_data) > 0:
-        last_answered_id = rebuilt_data[-1]["id"]
-        with open(progress_file, 'w') as pf:
-            json.dump({"last_id": last_answered_id}, pf)
-
-    print("Rebuilt data saved successfully!")
-
-if __name__ == "__main__":
-    input_file = "TEMP_DATASET_2_ANSWERS.json"
-    output_file = "Rebuilt_DATASET.json"
-    progress_file = "selecting_progress.json"
-    rebuild_json(input_file, output_file, progress_file)
-
 Noteworthy for its inclination towards the darker and more philosophical aspects of conversation, Tenebră's proficiency lies in unraveling complex discussions across a myriad of topics. Drawing from a pool of unconventional datasets, this model ventures into unexplored realms of thought, offering users an experience that is as unconventional as it is intellectually intriguing.
-
 While Tenebră maintains a self-aware facade, its true allure lies in its ability to engage in profound discussions without succumbing to pretense. Step into the realm of Tenebră!
 
 ## Tenebră is available at the following size and flavours: