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ada-flo/
nlp-hack-debate-xlmr-lstm

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ada-flo/nlp-hack-debate-xlmr-lstm

Bilingual (English + Korean) LSTM seq2seq debate chatbot. The encoder is a frozen XLM-RoBERTa-base providing contextual hidden states; the decoder is an LSTM with Bahdanau attention. Trained on debate-shape (topic, PRO, CON) records plus discourse corpora for fluency.

Test metrics

BLEU: 3.128
Loss: 5.515
Perplexity: 248.4
n_eval: 5,443

Training history

Epoch	Train loss	Valid loss	Valid PPL	Valid BLEU
1	5.959	5.902	365.6	2.338
2	5.826	5.813	334.6	2.448
3	5.731	5.745	312.7	2.715
4	5.652	5.661	287.4	2.458
5	5.586	5.618	275.4	2.666

Architecture

Encoder: xlmr (frozen XLM-RoBERTa-base)
Decoder: LSTM with Bahdanau attention
Embed dim: 256, hidden dim: 512
Encoder layers: 2, decoder layers: 1
Tokenizer: SentencePiece, 32k shared bilingual vocab (ships as spm.model)

Training config

Init from: checkpoints/xlmr-1777297357/best.pt
Epochs: 5, batch size: 16, lr: 0.0003
Optimizer: Adam, label smoothing: 0.1
Max src/tgt length: 128/128

Files

best.pt — model weights (state_dict saved as model_state inside the checkpoint dict)
spm.model — SentencePiece tokenizer (32k shared bilingual vocab)
args.json — full training config
history.json — per-epoch validation metrics
test_metrics.json — final held-out test metrics

Inference

Clone the source repo (https://github.com/ada-flo/nlp-hack), then:

python

import torch, sentencepiece as spm
from huggingface_hub import hf_hub_download
from src.model.lstm_seq2seq import Seq2Seq

ckpt_path = hf_hub_download("ada-flo/nlp-hack-debate-xlmr-lstm", "best.pt")
sp_path = hf_hub_download("ada-flo/nlp-hack-debate-xlmr-lstm", "spm.model")

sp = spm.SentencePieceProcessor()
sp.Load(sp_path)
ckpt = torch.load(ckpt_path, map_location="cuda", weights_only=False)

model = Seq2Seq(
    vocab_size=sp.get_piece_size(),
    embed_dim=256, hidden_dim=512,
    enc_layers=2, dec_layers=1,
    dropout=0.0, encoder_type="xlmr",
).cuda().eval()
model.load_state_dict(ckpt["model_state"], strict=False)

Data

Training data is published separately as a HF dataset. See the source repo (https://github.com/ada-flo/nlp-hack) for the preprocessing pipeline.

License

CC BY 4.0. Underlying corpora retain their original licenses; consult the source repo for details before commercial use.