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Model: mario-rc/emotional-gpt2-medium Source: Original Platform
2026-07-18 15:56:27 +08:00
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+---
+license: mit
+base_model: gpt2-medium
+library_name: transformers
+pipeline_tag: text-generation
+tags:
+- text-generation
+- gpt2
+- dialogue
+- emotion
+- causal-lm
+language:
+- en
+datasets:
+- DailyDialog
+metrics:
+- perplexity
+---
+
+# Emotional GPT-2 Medium
+
+`emotional-gpt2-medium` is a [GPT-2 Medium](https://huggingface.co/openai-community/gpt2-medium)
+causal language model fine-tuned for emotion-conditioned dialogue generation
+with DailyDialog-derived data.
+
+GitHub repository: [`Mario-RC/emotional-gpt`](https://github.com/Mario-RC/emotional-gpt)
+
+## Model Details
+
+- Base model: [`gpt2-medium`](https://huggingface.co/openai-community/gpt2-medium)
+- Architecture: `GPT2LMHeadModel`
+- Task: text generation
+- Context length: 1024 tokens
+- Parameters: 354.8M
+- Evaluation perplexity: 10.0080
+
+## Model Comparison
+
+| Model | Base model | Parameters | Evaluation perplexity |
+| :--- | :---: | :---: | :---: |
+| [Emotional DistilGPT2](https://huggingface.co/mario-rc/emotional-distilgpt2) | [`distilgpt2`](https://huggingface.co/distilgpt2) | 81.9M | 15.3322 |
+| [Emotional GPT-2](https://huggingface.co/mario-rc/emotional-gpt2) | [`gpt2`](https://huggingface.co/openai-community/gpt2) | 124.4M | 12.9404 |
+| [Emotional GPT-2 Medium](https://huggingface.co/mario-rc/emotional-gpt2-medium) | [`gpt2-medium`](https://huggingface.co/openai-community/gpt2-medium) | 354.8M | 10.0080 |
+| [Emotional GPT-2 Large](https://huggingface.co/mario-rc/emotional-gpt2-large) | [`gpt2-large`](https://huggingface.co/openai-community/gpt2-large) | 774.0M | 7.4115 |
+| [Emotional DialoGPT Small](https://huggingface.co/mario-rc/emotional-dialogpt-small) | [`microsoft/DialoGPT-small`](https://huggingface.co/microsoft/DialoGPT-small) | 124.4M | 13.0488 |
+| [Emotional DialoGPT Medium](https://huggingface.co/mario-rc/emotional-dialogpt-medium) | [`microsoft/DialoGPT-medium`](https://huggingface.co/microsoft/DialoGPT-medium) | 354.8M | 10.5130 |
+| [Emotional DialoGPT Large](https://huggingface.co/mario-rc/emotional-dialogpt-large) | [`microsoft/DialoGPT-large`](https://huggingface.co/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:
+
+```text
+<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:
+
+```text
+<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:
+
+```text
+<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-medium"
+
+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.