transformers pytorch t5 text2text-generation summarization en license:mit model-index autotrain_compatible text-generation-inference endpoints_compatible region:us
---
language: en
license: mit
tags:
- summarization
model-index:
- name: SamuelAllen123/t5-efficient-large-nl36_fine_tune_sum_V2
  results:
  - task:
      type: summarization
      name: Summarization
    dataset:
      name: samsum
      type: samsum
      config: samsum
      split: test
    metrics:
    - type: rouge
      value: 50.5049
      name: ROUGE-1
      verified: true
    - type: rouge
      value: 25.6469
      name: ROUGE-2
      verified: true
    - type: rouge
      value: 41.7544
      name: ROUGE-L
      verified: true
    - type: rouge
      value: 46.2055
      name: ROUGE-LSUM
      verified: true
    - type: loss
      value: 1.5158178806304932
      name: loss
      verified: true
    - type: gen_len
      value: 24.0342
      name: gen_len
      verified: true
  - task:
      type: summarization
      name: Summarization
    dataset:
      name: cnn_dailymail
      type: cnn_dailymail
      config: 3.0.0
      split: test
    metrics:
    - type: rouge
      value: 34.4055
      name: ROUGE-1
      verified: true
    - type: rouge
      value: 14.127
      name: ROUGE-2
      verified: true
    - type: rouge
      value: 24.3353
      name: ROUGE-L
      verified: true
    - type: rouge
      value: 31.6582
      name: ROUGE-LSUM
      verified: true
    - type: loss
      value: 2.4456119537353516
      name: loss
      verified: true
    - type: gen_len
      value: 45.928
      name: gen_len
      verified: true
  - task:
      type: summarization
      name: Summarization
    dataset:
      name: samsum
      type: samsum
      config: samsum
      split: train
    metrics:
    - type: rouge
      value: 54.933
      name: ROUGE-1
      verified: true
    - type: rouge
      value: 31.7965
      name: ROUGE-2
      verified: true
    - type: rouge
      value: 47.0057
      name: ROUGE-L
      verified: true
    - type: rouge
      value: 51.2027
      name: ROUGE-LSUM
      verified: true
    - type: loss
      value: 1.130684494972229
      name: loss
      verified: true
    - type: gen_len
      value: 23.7989
      name: gen_len
      verified: true
  - task:
      type: summarization
      name: Summarization
    dataset:
      name: scientific_papers
      type: scientific_papers
      config: pubmed
      split: train
    metrics:
    - type: rouge
      value: 23.6698
      name: ROUGE-1
      verified: true
      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMTg4OTMwYjkyNmU1ZjdmN2Q4MWE4YzFkZjUyMDZhNDNhYjBkODg3ZjI5NDQxMTcyNDUyMzkwNDZlNjNhZGRiOSIsInZlcnNpb24iOjF9.0kRK7iA642z0YWAH81v1_-pil6TyM3bezGfZtqGev5O7AgGkxzfQaIDNhkVVvVIJdUPJFD7L36XyLx3AWO5BCQ
    - type: rouge
      value: 7.5691
      name: ROUGE-2
      verified: true
      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiY2Q2MDc1ZjZlYjRmZDZkNjY3MmFhODAzZWUwZjA1M2RlZGUwYTY2ZjM2ZTM1NzQ3YjAxMDFiMWZlMGMwNTgyOCIsInZlcnNpb24iOjF9._Y59aEEGLn0Ij622V8Rwljp-h4uTuCfoPgJdvMN6GvCyKRzwugHo8tedfTpbTAb6cicjiWjKvKurqXTjpw1KAw
    - type: rouge
      value: 15.6071
      name: ROUGE-L
      verified: true
      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNjMwM2Q2ODYwZWE4MzNhNDNlNzlhNjU2NGUxYjlhNDM3MzM5MmJjNzU4YTYxNzI4ZmQ3YzQ1YjMzMDZkMTQ4ZCIsInZlcnNpb24iOjF9.zyfiVsuCEXCTkGAqNxCZ8hTKVxAE0JmJRbNZ04HoBi7qYFB13_7JTB6tOvAEH34W-2yvpOs4cBsFqtXg7RvnCA
    - type: rouge
      value: 21.4565
      name: ROUGE-LSUM
      verified: true
      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiOTE4MjVlZjI5NDBkZjRmODA3MmIzY2I0YWUwZjEyMzYwNjFjNTY3N2NjMmY3ZThlODBjN2VhZWZlODliZmEyZSIsInZlcnNpb24iOjF9.RFZbr5R9cJtrhzWMKys62fiBxKv8MYe6_115NBjEZ6wOwzVih5SdJE8r2EK-1wdCMF_jLGPYQvZ-zyj3KHGWCw
    - type: loss
      value: 3.9369945526123047
      name: loss
      verified: true
      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNTc3MzMwYTg5OWIyZGQxNGJlYzExNTY0MjUyY2M5M2NiOGQ2ODI0MWFiMzJjYWY4ZGNkZmY2MmUyZjVjODRiYSIsInZlcnNpb24iOjF9.iDxSfTwZRV5VboHLjF4a47kPXagG7bY78WIejIM37ykpksXxVYssZlmK6UxtkEmZuWypqbQjz6oOjTjy6x3tDQ
    - type: gen_len
      value: 65.9987
      name: gen_len
      verified: true
      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiODdmYzFiNzU3N2VlMWMyMGEwZmFkZmExZWRlN2NjNWI3ZGJjNmYzMWExYWM5MWY2MzJkMmY0ZGE2NjFjMjRjYyIsInZlcnNpb24iOjF9.3ByM1s1Ux-PDBBnf6i3FUtFLzpmZXcikIfrsR3vTIi9567r789Wm8sW81blFHNfnST-ZHQxPKJOuv4ho8S4eCg
---
*NOT SELF REPORTED VALUES FOR THE LEADERBOARD, I HAVE NO CLUE WHY ITS BROKE. CHECK PULL REQUEST*

Use summarization without adding summarize to the start of the string.

Trained on Samsum train split. 

Parameters for training:

no_decay = ["bias", "LayerNorm.weight", "layer_norm.weight"]
optimizer_grouped_parameters = [
    {
        "params": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)],
        "weight_decay": 0.0,
    },
    {
        "params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)],
        "weight_decay": 0.0,
    },
]

lr = 0.00005
optimizer = torch.optim.RAdam(optimizer_grouped_parameters, lr=lr)

lr_scheduler = get_scheduler(
        name="linear",
        optimizer=optimizer,
        num_warmup_steps=0,
        num_training_steps=50005)

This was only for 10K steps with a batch size of 10

If you want more info, feel free to message me or email me at:
samuelfipps@gmail.com