File size: 2,169 Bytes
d31084e 83716b7 d31084e 83716b7 d31084e 83716b7 d31084e 83716b7 d31084e 83716b7 d31084e 83716b7 c260988 b7cf562 59d4418 553d60b 59d4418 553d60b f0ee7a8 553d60b |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 |
---
language: en
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:
- name: ROUGE-1
type: rouge
value: 50.4987
verified: true
- name: ROUGE-2
type: rouge
value: 25.6888
verified: true
- name: ROUGE-L
type: rouge
value: 41.7283
verified: true
- name: ROUGE-LSUM
type: rouge
value: 46.2626
verified: true
- name: loss
type: loss
value: 1.5158178806304932
verified: true
- name: gen_len
type: gen_len
value: 24.0342
verified: true
- task:
type: summarization
name: Summarization
dataset:
name: cnn_dailymail
type: cnn_dailymail
config: 3.0.0
split: test
metrics:
- name: ROUGE-1
type: rouge
value: 34.4055
verified: true
- name: ROUGE-2
type: rouge
value: 14.127
verified: true
- name: ROUGE-L
type: rouge
value: 24.3353
verified: true
- name: ROUGE-LSUM
type: rouge
value: 31.6582
verified: true
- name: loss
type: loss
value: 2.4456119537353516
verified: true
- name: gen_len
type: gen_len
value: 45.928
verified: true
---
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
More details coming soon |