Update README.md

README.md

@@ -1,3 +1,58 @@
 ---
+language:
+- en
 license: apache-2.0
+tags:
+- dialogue state tracking
+- task-oriented dialog
+
 ---
+
+# roberta-base-trippy-dst-multiwoz21
+
+This is a TripPy model trained on [MultiWOZ 2.1](https://github.com/budzianowski/multiwoz) for use in [ConvLab-3](https://github.com/ConvLab/ConvLab-3).
+This model predicts informable slots, requestable slots, general actions and domain indicator slots.
+Expected joint goal accuracy for MultiWOZ 2.1 is in the range of 55-56\%.
+
+For information about TripPy DST, refer to [TripPy: A Triple Copy Strategy for Value Independent Neural Dialog State Tracking](https://aclanthology.org/2020.sigdial-1.4/).
+
+The training and evaluation code is available at the official [TripPy repository](https://gitlab.cs.uni-duesseldorf.de/general/dsml/trippy-public).
+
+## Training procedure
+
+The model was trained on MultiWOZ 2.1 data via supervised learning using the [TripPy codebase](https://gitlab.cs.uni-duesseldorf.de/general/dsml/trippy-public).
+MultiWOZ 2.1 data was loaded via ConvLab-3's unified data format dataloader.
+The pre-trained encoder is [RoBERTa](https://arxiv.org/abs/1907.11692) (base).
+Fine-tuning the encoder and training the DST specific classification heads was conducted for 10 epochs.
+
+### Training hyperparameters
+
+```
+python3 run_dst.py \
+  --task_name="unified" \
+  --model_type="roberta" \
+  --model_name_or_path="roberta-base" \
+  --dataset_config=dataset_config/unified_multiwoz21.json \
+  --do_lower_case \
+  --learning_rate=1e-4 \
+  --num_train_epochs=10 \
+  --max_seq_length=180 \
+  --per_gpu_train_batch_size=24 \
+  --per_gpu_eval_batch_size=32 \
+  --output_dir=results \
+  --save_epochs=2 \
+  --eval_all_checkpoints \
+  --logging_steps=10 \
+  --warmup_proportion=0.1 \
+  --adam_epsilon=1e-6 \
+  --weight_decay=0.01 \
+  --label_value_repetitions \
+  --swap_utterances \
+  --append_history \
+  --use_history_labels \
+  --fp16 \
+  --do_train \
+  --predict_type=dummy \
+  --seed=42
+```
+