flan-t5-small-instructiongen
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 |
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Dataset used to train pszemraj/flan-t5-small-instructiongen
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Evaluation results
- Rouge1 on pszemraj/fleece2instructionsvalidation set self-reported52.201