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 | |
- task: | |
type: summarization | |
name: Summarization | |
dataset: | |
name: samsum | |
type: samsum | |
config: samsum | |
split: train | |
metrics: | |
- name: ROUGE-1 | |
type: rouge | |
value: 54.933 | |
verified: true | |
- name: ROUGE-2 | |
type: rouge | |
value: 31.7965 | |
verified: true | |
- name: ROUGE-L | |
type: rouge | |
value: 47.0057 | |
verified: true | |
- name: ROUGE-LSUM | |
type: rouge | |
value: 51.2027 | |
verified: true | |
- name: loss | |
type: loss | |
value: 1.130684494972229 | |
verified: true | |
- name: gen_len | |
type: gen_len | |
value: 23.7989 | |
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 |