transformers pytorch bart text2text-generation summarization dataset:samsum model-index autotrain_compatible endpoints_compatible region:us
---
datasets:
- samsum
pipeline_tag: summarization
widget:
- text: "Laurie: So, what are your plans for this weekend?\nChristie: I don’t know.\
    \ Do you want to get together or something?\nSarah: How about going to see a movie?\
    \ Cinemax 26 on Carson Boulevard is showing Enchanted. Laurie: That sounds like\
    \ a good idea. Maybe we should go out to eat beforehand.\nSarah: It is fine with\
    \ me. Where do you want to meet?\nChristie: Let’s meet at Summer Pizza House.\
    \ I have not gone there for a long time.\nLaurie: Good idea again. I heard they\
    \ just came up with a new pizza. It should be good because Summer Pizza House\
    \ always has the best pizza in town.\nSarah: When should we meet?\nChristie: Well,\
    \ the movie is shown at 2:00PM, 4:00PM, 6:00PM and 8:00PM.\nLaurie: Why don’t\
    \ we go to the 2:00PM show? We can meet at Summer Pizza House at noon. That will\
    \ give us plenty of time to enjoy our pizza.\nSarah: My cousin Karen is in town.\
    \ Can I bring her along? I hate to leave her home alone.\nChristie: Karen is in\
    \ town? Yes, bring her along. Laurie, you remember Karen? We met her at Sara’s\
    \ high school graduation party two years ago.\nLaurie: I do not quite remember\
    \ her. What does she look like?\nSarah: She has blond hair, she is kind of slender,\
    \ and she is about your height.\nLaurie: She wears eyeglasses, right?\nSarah:\
    \ Yes, and she was playing the piano off and on during the party.\nLaurie: I remember\
    \ her now. Yes, do bring her along Sara. She is such a nice person, and funny\
    \ too.\nSarah: She will be happy to meet both of you again.\nChristie: What is\
    \ she doing these days?\nSarah: She graduated last June, and she will start her\
    \ teaching career next week when the new school term begins.\nLaurie: What grade\
    \ is she going to teach?\nSarah: She will teach kindergarten. She loves working\
    \ with kids, and she always has such a good rapport with them\nChristie: Kindergarten?\
    \ She must be a very patient person. I always think kindergarten is the most difficult\
    \ class to teach. Most of the kids have never been to school, and they have e\
    \ never been away from mommy for long.\nSarah:  I think Karen will do fine. She\
    \ knows how to handle young children\nLaurie: I think the first few weeks will\
    \ be tough. However, once the routine is set, it should not be too difficult to\
    \ teach kindergarten.\nChristie: You are right. The kids might even look forward\
    \ to going to school since they have so many friends to play with.\nSarah: There\
    \ are so many new things for them to do at school too. They do a lot of crafts\
    \ in kindergarten. I am always amazed by the things kindergarten teachers do.\
    \ \nLaurie: Yes, I have seen my niece come home with so many neat stuff.\nChristie:\
    \ Maybe we can ask Karen to show us some of the things that we can do for this\
    \ Halloween.\nLaurie: Maybe we can stop by the craft store after the movie. What\
    \ do you think, Sara?\nSarah: I will talk to her. I think she will like that.\
    \ It will help her with school projects when Halloween comes.\nChristie: Michael’s\
    \ is a good store for crafts. It always carries a variety of things, and you can\
    \ find almost anything there.\nLaurie: There is a Michaels store not far away\
    \ from Cinemax 26. I believe it is just around the corner, on Pioneer Avenue.\
    \ We can even walk over there.\nSarah: So, we plan to meet for pizza at noon,\
    \ go to the movies at two, and shop at Michael’s afterward. Right?\nLaurie and\
    \ Christie: Yes.      \n"
model-index:
- name: bart-large-cnn-samsum
  results:
  - task:
      type: summarization
      name: Conversation Summarization
    dataset:
      name: 'SAMSum Corpus: A Human-annotated Dialogue Dataset for Abstractive Summarization'
      type: samsum
    metrics:
    - type: rogue-1
      value: 54.8764
      name: Validation ROGUE-1
    - type: rogue-2
      value: 29.6869,
      name: Validation ROGUE-2
    - type: rogue-l
      value: 44.9874
      name: Validation ROGUE-L
    - type: loss
      value: 1.47812
      name: loss
---
