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README.md

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+---
+tags:
+- conversational
+license: gpl-3.0
+---
+## A conversational agent with many personalities (PersonaGPT)
+PersonaGPT is an open-domain conversational agent capable of decoding _personalized_ responses based on input . 
+It builds on the [DialoGPT-medium](https://huggingface.co/microsoft/DialoGPT-medium) pretrained model based on the [GPT-2](https://github.com/openai/gpt-2) architecture. 
+This model is trained on the [Persona-Chat](https://arxiv.org/pdf/1801.07243) dataset, with added special tokens to better distinguish between conversational history and personality traits for dyadic conversations. Furthermore, some active learning was used to train the model to do _controlled_ decoding based on certain "action codes" (e.g., "talk about work", "ask about favorite music"). 
+
+
+## Full Repo
+
+Preprocessing, training and implementation details can be found in the [personaGPT repo](https://github.com/af1tang/personaGPT).
+
+### How to Use
+
+
+1. Load the model and define some helper functions.
+
+```python
+from transformers import GPT2Tokenizer, GPT2LMHeadModel
+import torch
+tokenizer = AutoTokenizer.from_pretrained("af1tang/personaGPT")
+model = AutoModelForCausalLM.from_pretrained("af1tang/personaGPT")
+if torch.cuda.is_available():
+	model = model.cuda()
+## utility functions ##
+flatten = lambda l: [item for sublist in l for item in sublist]
+
+def to_data(x):
+    if torch.cuda.is_available():
+        x = x.cpu()
+    return x.data.numpy()
+
+def to_var(x):
+    if not torch.is_tensor(x):
+        x = torch.Tensor(x)
+    if torch.cuda.is_available():
+        x = x.cuda()
+    return x
+
+def display_dialog_history(dialog_hx):
+    for j, line in enumerate(dialog_hx):
+        msg = tokenizer.decode(line)
+        if j %2 == 0:
+            print(">> User: "+ msg)
+        else:
+            print("Bot: "+msg)
+            print()
+
+def generate_next(bot_input_ids, do_sample=True, top_k=10, top_p=.92,
+                  max_length=1000, pad_token=tokenizer.eos_token_id):
+    full_msg = model.generate(bot_input_ids, do_sample=True,
+                                              top_k=top_k, top_p=top_p, 
+                                              max_length=max_length, pad_token_id=tokenizer.eos_token_id)
+    msg = to_data(full_msg.detach()[0])[bot_input_ids.shape[-1]:]
+    return msg
+```
+
+2. Give your chatbot partner a set of personalities. 
+
+
+```python
+# get personality facts for conversation
+personas = []
+for i in range(3):
+    response = input(">> Fact %d: "%(i+1))+ tokenizer.eos_token
+    personas.append(response)
+personas = tokenizer.encode(''.join(['<|p2|>'] + personas + ['<|sep|>'] + ['<|start|>']))
+```
+
+3. The first use of PersonaGPT is to do _personalized_ dialog generation. Use the following loop to interact with the model.
+
+```python
+# converse for 8 turns
+dialog_hx = []
+for step in range(8):
+    # encode the user input
+    user_inp = tokenizer.encode(input(">> User: ") + tokenizer.eos_token)
+    # append to the chat history
+    dialog_hx.append(user_inp)
+        
+    # generated a response while limiting the total chat history to 1000 tokens, 
+    bot_input_ids = to_var([personas + flatten(dialog_hx)]).long()
+    msg = generate_next(bot_input_ids)
+    dialog_hx.append(msg)
+    print("Bot: {}".format(tokenizer.decode(msg, skip_special_tokens=True)))
+```
+
+
+Example of personalized decoding:
+
+| | Persona Facts |
+|---|---|
+| 1 | i am 64 years old but feel quite young. |
+| 2 | i try to eat healthy but limit mcdonalds to once a week. |
+| 3 | i regret working as a doctor for the last 20 years. |
+| 4 | my secret hobby is making self-help youtube videos. |
+| 5 | i've been to spain many times and wish i could go again. |
+
+| Role | Response|
+|---|---|
+| Human | yo whats good fam? |
+| Bot | hello, how are you doing? |
+| Human | not bad what you doing today?|
+| Bot | eating a lot healthier now that i'm in my 60s |
+| Human | i'm more of a fast food guy |
+| Bot | me too but i'm very healthy so i try to avoid it |
+| Human | what do you do for fun? |
+| Bot | i make videos for fun and eat very healthy |
+| Human | nice what kind of videos? |
+| Bot | i love making videos about self help stuff |
+| Human | i think i need some help | 
+| Bot | what kind of help would you like to get |
+| Human | i'm 21 and feel lost in life | 
+| Bot | i can relate, i'm only 65 |
+
+
+4. The second use of PersonaGPT is to do _controlled response generation_. Use the following program to interact with the model.
+
+```python
+## available actions ##
+action_space = [ 'ask about kids.', "ask about pets.", 'talk about work.', 
+               'ask about marital status.', 'talk about travel.', 'ask about age and gender.',
+        'ask about hobbies.', 'ask about favorite food.', 'talk about movies.', 
+        'talk about music.', 'talk about politics.']
+# converse for 8 turns
+dialog_hx = []
+for step in range(8):
+	# choose an action
+    act = None
+    while act not in action_space:
+        display_dialog_history(dialog_hx)
+        print()
+        print(" actions: ")
+        for k,v in enumerate(action_space): print(k,v)
+        try:
+            act = action_space[int(input(" input [0-10]: " ))]
+        except:
+            act = None
+    print()
+    # format into prefix code
+    action_prefix = tokenizer.encode(''.join(['<|act|> '] + [act] + ['<|p1|>'] + [] + ['<|sep|>'] + ['<|start|>']))
+    bot_input_ids = to_var([action_prefix + flatten(dialog_hx)]).long()
+    
+    # generate query conditioned on action
+    msg = generate_next(bot_input_ids, top_k=top_k, top_p=top_p, max_length=max_length)
+    dialog_hx.append(msg)
+    
+    # generate bot response
+    bot_input_ids = to_var([personas+ flatten(dialog_hx)]).long()
+    msg = generate_next(bot_input_ids, top_k=top_k, top_p=top_p, max_length=max_length)
+    dialog_hx.append(msg)
+display_dialog_history(dialog_hx)
+```
+
+
+Example of controlled response generation: 
+
+
+| | Bot 2's Persona Facts |
+|---|---|
+| 1 | i am a quiet engineer. |
+| 2 | i'm single and am looking for love. |
+| 3 | sadly, i don't have any relatable hobbies. |
+| 4 | luckily, however, i am tall and athletic. |
+| 5 | on friday nights, i watch re-runs of the simpsons alone. |
+
+| Role | Action | Response |
+|---|---|---|
+| Bot 1 | talk about movies | hello do you like movies? |
+| Bot 2 | - | i love the simpsons. how about you? |
+| Bot 1 | talk about music | i like the walking dead. do you play an instrument? |
+| Bot 2 | - | yes i play the violin. |
+| Bot 1 | ask about marital status | are you married or single? |
+| Bot 2 | - | i am single but i am looking for love. |
+| Bot 1 | talk about work | my job is working in a factory. what's your occupation? |
+| Bot 2 | - | engineer. i'm very quiet so no one hears me. |
+| Bot 1 | talk about hobbies | do you have any hobbies? |
+| Bot 2 | - | i watch reruns of the simpsons. | 
+| Bot 1 | ask about favorite food | what's your favorite food? |
+| Bot 2 | - | i love pizza. how about yourself? |
+| Bot 1 | ask about pets | i also love pizza. do you like animals? | 
+| Bot 2 | - | i have two dogs. what is your occupation? | 
+| Bot 1 | talk about work | i'm a factory worker. what's your dream job? |
+| Bot 2 | - | i'd love to be a writer one day. |

added_tokens.json

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+{"<|sep|>": 50257, "<|cls|>": 50258, "<|start|>": 50259, "<|p1|>": 50260, "<|p2|>": 50261}

config.json

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+{
+  "activation_function": "gelu_new",
+  "architectures": [
+    "GPT2LMHeadModel"
+  ],
+  "attn_pdrop": 0.1,
+  "bos_token_id": 50256,
+  "embd_pdrop": 0.1,
+  "eos_token_id": 50256,
+  "initializer_range": 0.02,
+  "layer_norm_epsilon": 1e-05,
+  "model_type": "gpt2",
+  "n_ctx": 1024,
+  "n_embd": 1024,
+  "n_head": 16,
+  "n_layer": 24,
+  "n_positions": 1024,
+  "resid_pdrop": 0.1,
+  "summary_activation": null,
+  "summary_first_dropout": 0.1,
+  "summary_proj_to_labels": true,
+  "summary_type": "cls_index",
+  "summary_use_proj": true,
+  "vocab_size": 50262
+}

pytorch_model.bin

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d7582d23d2d19e37339c15685b171dce5e6ea98787a82b40ff3c4ad8011a87a6
+size 1444551925

special_tokens_map.json

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+{"bos_token": {"content": "<|endoftext|>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true}, "eos_token": {"content": "<|endoftext|>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true}, "unk_token": {"content": "<|endoftext|>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true}, "sep_token": "<|sep|>", "pad_token": "<|endoftext|>", "cls_token": "<|cls|>", "additional_special_tokens": ["<|start|>", "<|p1|>", "<|p2|>"]}

tokenizer_config.json

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+{"pad_token": "<|endoftext|>", "cls_token": "<|cls|>", "sep_token": "<|sep|>", "special_tokens_map_file": null, "full_tokenizer_file": null}