Instead of generating questions from text, generate instructions for LLMs!
This model is a fine-tuned version of
google/flan-t5-small
on the None dataset.
It achieves the following results on the evaluation set:
Loss: 1.3401
Rouge1: 52.201
Rouge2: 35.6154
Rougel: 50.2334
Rougelsum: 50.338
Gen Len: 14.0450
Intended uses & limitations
This is just a
small
model/example. There is likely to be even better performance with larger models (ex
pszemraj/bart-base-instructiongen)
generalizes better)
Additionally, this was trained on a dataset of
only
instructions+outputs, with the
inputs
filtered out. This means that text of
1) cookies and cream 2) chocolate chip 3) mint chip 4) oreo
will
not
get you
"Rank the following ice cream flavors: oreo, mint chip, chocolate chip, cookies and cream"
.
Training and evaluation data
See the linked dataset
pszemraj/fleece2instructions
- it is a filtered/formatted version of
tatsu-lab/alpaca
to generate instructions for arbitrary text.
Some of the API examples are intentionally weird to demonstrate the generalizability of the model.
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
learning_rate: 8e-05
train_batch_size: 8
eval_batch_size: 1
seed: 42
distributed_type: multi-GPU
gradient_accumulation_steps: 16
total_train_batch_size: 128
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.02
num_epochs: 2.0
Training results
Training Loss
Epoch
Step
Validation Loss
Rouge1
Rouge2
Rougel
Rougelsum
Gen Len
1.6161
1.0
181
1.3714
51.1003
34.5701
49.1277
49.2466
13.8357
1.539
2.0
362
1.3401
52.201
35.6154
50.2334
50.338
14.0450
Runs of pszemraj flan-t5-small-instructiongen on huggingface.co
43
Total runs
0
24-hour runs
-2
3-day runs
2
7-day runs
30
30-day runs
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