Model	Base model	Parameters	Evaluation perplexity
Emotional DistilGPT2	`distilgpt2`	81.9M	15.3322
Emotional GPT-2	`gpt2`	124.4M	12.9404
Emotional GPT-2 Medium	`gpt2-medium`	354.8M	10.0080
Emotional GPT-2 Large	`gpt2-large`	774.0M	7.4115
Emotional DialoGPT Small	`microsoft/DialoGPT-small`	124.4M	13.0488
Emotional DialoGPT Medium	`microsoft/DialoGPT-medium`	354.8M	10.5130
Emotional DialoGPT Large	`microsoft/DialoGPT-large`	774.0M	8.6719

Training

The fine-tuning run used the following setup:

Framework: Hugging Face Transformers
Training data: data/gpt-dialogues/train.txt; evaluation data: data/gpt-dialogues/dev.txt, built from DailyDialog CSV resources
Epochs: 4
Train/eval batch size per GPU: 6 / 6
Gradient accumulation steps: 1
Effective training batch size: 6
Learning rate: 1e-5
Max gradient norm: 1.0
Objective: line-by-line causal language modeling
Seed: 42
Checkpointing/logging: every 5000 optimizer steps; last checkpoint kept
Memory optimization: gradient checkpointing not used

Training Format

Training examples use adjacent DailyDialog utterance pairs with explicit source and target emotion labels:

<bos><source_emotion>source utterance<sep><target_emotion>target utterance<|endoftext|>

Prompt Format

At generation time, the prompt should include the source utterance and the desired target emotion:

<bos><source_emotion>source utterance<sep><target_emotion>

Prompt and training tags:

<bos> marks the beginning of one formatted dialogue example.
<source_emotion> is a placeholder for one emotion label describing the input/source utterance, for example <fear>.
source utterance is the user/input text.
<sep> separates the source side from the response side.
<target_emotion> is a placeholder for the emotion you want the generated response to follow, for example <happiness>.
target utterance is the response text generated by the model.
<|endoftext|> marks the end of one example. GPT-2 uses this as its native end-of-text/eos token, and generation can stop when this token is produced.

Emotion conditioning: replace <source_emotion> and <target_emotion> in the template with one of the model's literal emotion tokens in each position.

Supported emotion labels:

<no emotion>
<anger>
<disgust>
<fear>
<happiness>
<sadness>
<surprise>

For example:

<bos><fear>I just started a new job and I am a bit nervous.<sep><happiness>

This means: the source utterance expresses fear, and the requested response should be conditioned toward happiness.

How to Use

python

from transformers import AutoModelForCausalLM, AutoTokenizer

repo_id = "mario-rc/emotional-gpt2"

tokenizer = AutoTokenizer.from_pretrained(repo_id)
model = AutoModelForCausalLM.from_pretrained(repo_id)
model.config.pad_token_id = tokenizer.pad_token_id

prompt = "<bos><fear>I just started a new job and I am a bit nervous.<sep><happiness>"
inputs = tokenizer(prompt, return_tensors="pt")

outputs = model.generate(
    **inputs,
    do_sample=True,
    max_new_tokens=80,
    temperature=0.8,
    top_p=0.95,
    pad_token_id=tokenizer.pad_token_id,
    eos_token_id=tokenizer.eos_token_id,
)

generated = outputs[0][inputs["input_ids"].shape[-1]:]
response = tokenizer.decode(generated, skip_special_tokens=False)
response = response.split(tokenizer.eos_token, 1)[0].strip()

emotion_labels = [
    "<no emotion>",
    "<anger>",
    "<disgust>",
    "<fear>",
    "<happiness>",
    "<sadness>",
    "<surprise>",
]

for label in emotion_labels:
    if response.startswith(label):
        response = response[len(label):].strip()
        break

print(response)

Limitations

The model is intended for experimental dialogue/text generation. Generated text may be inaccurate, biased, repetitive, or emotionally inappropriate, and should be reviewed before user-facing use.