Create README.md

README.md

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+---
+language:
+- en
+license: apache-2.0
+tags:
+- t5-small
+- text2text-generation
+- natural language generation
+- conversational system
+- task-oriented dialog
+datasets:
+- ConvLab/multiwoz21
+metrics:
+- Slot Error Rate
+- sacrebleu
+
+model-index:
+- name: t5-small-nlg-user-multiwoz21
+  results:
+  - task:
+      type: text2text-generation
+      name: natural language generation
+    dataset:
+      type: ConvLab/multiwoz21
+      name: MultiWOZ 2.1
+      split: test
+      revision: 5f55375edbfe0270c20bcf770751ad982c0e6614
+    metrics:
+      - type: Slot Error Rate
+        value: 7.8
+        name: SER
+      - type: sacrebleu
+        value: 20.7
+        name: BLEU
+
+widget:
+- text: "[inform][taxi]([destination][Pizza Hut Fen Ditton],[departure][Saint John's college])\n\nuser: "
+- text: "[inform][attraction]([name][Nusha]);[inform][restaurant]([][])\n\nuser: "
+
+inference:
+  parameters:
+    max_length: 100
+
+---
+
+# t5-small-nlg-user-multiwoz21
+
+This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on [MultiWOZ 2.1](https://huggingface.co/datasets/ConvLab/multiwoz21) user utterances.
+
+Refer to [ConvLab-3](https://github.com/ConvLab/ConvLab-3) for model description and usage.
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 0.001
+- train_batch_size: 128
+- eval_batch_size: 64
+- seed: 42
+- gradient_accumulation_steps: 4
+- total_train_batch_size: 512
+- optimizer: Adafactor
+- lr_scheduler_type: linear
+- num_epochs: 10.0
+
+### Framework versions
+
+- Transformers 4.20.1
+- Pytorch 1.11.0+cu102
+- Datasets 2.3.2
+- Tokenizers 0.12.1