Instructions to use declare-lab/dialect with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use declare-lab/dialect with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("declare-lab/dialect") model = AutoModelForSeq2SeqLM.from_pretrained("declare-lab/dialect", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| license: mit | |
| widget: | |
| - text: "What is or could be the cause of target? <sep> target: Thanks. Will I be able to take a retest ? <sep> context: A: Did I do well on my test ?, <utt> B: Do you want to know the honest answer ?, <utt> A: Why wouldn't I want to know ?, <utt> B: You had pretty bad scores ., <utt> A: Exactly what do you mean by bad ?, <utt> B: You failed ., <utt> A: How'd I fail it ?, <utt> B: There are a couple of reasons why you didn't pass ., <utt> A: What did I do wrong ?, <utt> B: To sum it all up , you really just don't know how to drive ., <utt> A: Thanks. Will I be able to take a retest ?, <utt> B: Sure you can , in about two and a half weeks . " | |
| example_title: "Cause 1" | |
| - text: "What is or could be the cause of target? <sep> target: But she did and made me disappointed . <sep> context: A: David , why didn't you clean the room ?, <utt> B: I'm not in the mood ., <utt> A: Why are you feeling depressed ?, <utt> B: I was told my girlfriend was speaking ill of me. That's a real let-down ., <utt> A: I don t think she will do such a thing ., <utt> B: But she did and made me disappointed ., <utt> A: Oh , cheer up . A girlfriend is not everything ., <utt> B: But she means a lot to me ., <utt> A: Then forgive her mistake ., <utt> B: Oh . I just can't forget it " | |
| example_title: "Cause 2" | |
| - text: "What subsequent event happens or could happen following the target? <sep> target: Oh . I just can't forget it .<sep> context: A: David , why didn't you clean the room ?, <utt> B: I'm not in the mood ., <utt> A: Why are you feeling depressed ?, <utt> B: I was told my girlfriend was speaking ill of me. That \u2019 s a real let-down ., <utt> A: I don t think she will do such a thing ., <utt> B: But she did and made me disappointed ., <utt> A: Oh , cheer up . A girlfriend is not everything ., <utt> B: But she means a lot to me ., <utt> A: Then forgive her mistake ., <utt> B: Oh . I just can't forget it " | |
| example_title: "Subsequent Event 1" | |
| - text: "What subsequent event happens or could happen following the target? <sep> target: Sure you can , in about two and a half weeks . <sep> context: A: Did I do well on my test ?, <utt> B: Do you want to know the honest answer ?, <utt> A: Why wouldn't I want to know ?, <utt> B: You had pretty bad scores ., <utt> A: Exactly what do you mean by bad ?, <utt> B: You failed ., <utt> A: How'd I fail it ?, <utt> B: There are a couple of reasons why you didn't pass ., <utt> A: What did I do wrong ?, <utt> B: To sum it all up , you really just don't know how to drive ., <utt> A: Thanks. Will I be able to take a retest ?, <utt> B: Sure you can , in about two and a half weeks . " | |
| example_title: "Subsequent Event 2" | |
| - text: "What is the possible emotional reaction of the listener in response to target? <sep> target: Oh . I just can't forget it .<sep> context: A: David , why didn't you clean the room ?, <utt> B: I'm not in the mood ., <utt> A: Why are you feeling depressed ?, <utt> B: I was told my girlfriend was speaking ill of me. That \u2019 s a real let-down ., <utt> A: I don t think she will do such a thing ., <utt> B: But she did and made me disappointed ., <utt> A: Oh , cheer up . A girlfriend is not everything ., <utt> B: But she means a lot to me ., <utt> A: Then forgive her mistake ., <utt> B: Oh . I just can't forget it " | |
| example_title: "Emotional Reaction" | |
| - text: "What is or could be the motivation of target? <sep> target: Sure you can , in about two and a half weeks . <sep> context: A: Did I do well on my test ?, <utt> B: Do you want to know the honest answer ?, <utt> A: Why wouldn't I want to know ?, <utt> B: You had pretty bad scores ., <utt> A: Exactly what do you mean by bad ?, <utt> B: You failed ., <utt> A: How'd I fail it ?, <utt> B: There are a couple of reasons why you didn't pass ., <utt> A: What did I do wrong ?, <utt> B: To sum it all up , you really just don't know how to drive ., <utt> A: Thanks. Will I be able to take a retest ?, <utt> B: Sure you can , in about two and a half weeks . " | |
| example_title: "Motivation" | |
| ## DIALogue-level Commonsense Transformer (DIALeCT) | |
| The pretrained checkpoint for the paper [Multiview Contextual Commonsense Inference: A New Dataset and Task](https://arxiv.org/abs/2210.02890). | |
| The model is trained based on the [T5-large](https://huggingface.co/t5-large) checkpoint. | |
|  | |
| ## Datasets | |
| The dataset used to pretrain the model can be obtained from the [CICERO repo](https://github.com/declare-lab/CICERO) following instructions. The Contextualized Commonsense Inference in Dialogues v2 (CICEROv2) consists of annotated commonsense inferences including cause and emotional reaction, etc. The dialogues are from multiple datasets. | |
| | Dataset | #Dialogues| #Instances| | |
| | -------- | ----- | --------- | | |
| | DailyDialog| 1118| 3973| | |
| | MuTual| 1011 | 3384| | |
| | Dream| 250 | 994| | |
| ### Examples | |
| Some examples of generated results from the pretrained model (the zero-shot setting). | |
| **Subsequent Event** | |
| ``` | |
| What is or could be the subsequent event of the target? <sep> | |
| target: Oh . I just can't forget it .<sep> | |
| context: A: David , why didn't you clean the room ?, <utt> | |
| B: I'm not in the mood ., <utt> | |
| A: Why are you feeling depressed ?, <utt> | |
| B: I was told my girlfriend was speaking ill of me. That \u2019 s a real let-down ., <utt> | |
| A: I don t think she will do such a thing ., <utt> | |
| B: But she did and made me disappointed ., <utt> | |
| A: Oh , cheer up . A girlfriend is not everything ., <utt> | |
| B: But she means a lot to me ., <utt> | |
| A: Then forgive her mistake ., <utt> | |
| B: Oh . I just can't forget it | |
| ``` | |
| Predicted subsequent event: | |
| ``` | |
| David's girlfriend apologized to david for her mistake. | |
| ``` | |
| **Cause** | |
| ``` | |
| What is or could be the cause of target? <sep> | |
| target: Thanks. Will I be able to take a retest ? <sep> | |
| context: A: Did I do well on my test ?, <utt> | |
| B: Do you want to know the honest answer ?, <utt> | |
| A: Why wouldn't I want to know ?, <utt> | |
| B: You had pretty bad scores ., <utt> | |
| A: Exactly what do you mean by bad ?, <utt> | |
| B: You failed ., <utt> | |
| A: How'd I fail it ?, <utt> | |
| B: There are a couple of reasons why you didn't pass ., <utt> | |
| A: What did I do wrong ?, <utt> | |
| B: To sum it all up , you really just don't know how to drive ., <utt> | |
| A: Thanks. Will I be able to take a retest ?, <utt> | |
| B: Sure you can , in about two and a half weeks . | |
| ``` | |
| Predicted cause: | |
| ``` | |
| The speaker has failed the driving test. | |
| ``` | |
| **Emotional Reaction** | |
| ``` | |
| What is the possible emotional reaction of the listener in response to target? <sep> | |
| target: Oh . I just can't forget it .<sep> | |
| context: A: David , why didn't you clean the room ?, <utt> | |
| B: I'm not in the mood ., <utt> | |
| A: Why are you feeling depressed ?, <utt> | |
| B: I was told my girlfriend was speaking ill of me. That \u2019 s a real let-down ., <utt> | |
| A: I don t think she will do such a thing ., <utt> | |
| B: But she did and made me disappointed ., <utt> | |
| A: Oh , cheer up . A girlfriend is not everything ., <utt> | |
| B: But she means a lot to me ., <utt> | |
| A: Then forgive her mistake ., <utt> | |
| B: Oh . I just can't forget it | |
| ``` | |
| Predicted emotional reaction: | |
| ``` | |
| The listener is hopeful that david will forgive his girlfriend for her mistake. | |
| ``` | |
| ## Inference: | |
| The input text should be formatted as follows: | |
| ``` | |
| Question <sep> target: target_utt <sep> context: A: utterance 1 <utt> B: utterance 2 <utt> A: utterance 3 <utt> B: utterance 4 | |
| ``` | |
| Question: The question against which we want to make the inference. | |
| A, B are speaker identifiers | |
| The ```target_utt``` should be anyone between ```utterance 1, utterance 2, utterance 3, or utterance 4```. Do not use the speaker identifier in the ```target_utt``` | |
| Some samples are provided in the Hosted inference API box examples. | |
| ## BibTeX entry and citation info | |
| If you use the model, you can cite: | |
| ```bibtex | |
| @article{Shen2022MultiviewCC, | |
| title={Multiview Contextual Commonsense Inference: A New Dataset and Task}, | |
| author={Siqi Shen and Deepanway Ghosal and Navonil Majumder and Henry Lim and Rada Mihalcea and Soujanya Poria}, | |
| journal={ArXiv}, | |
| year={2022}, | |
| volume={abs/2210.02890} | |
| } | |
| ``` |