license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: t5-base-DreamBank-Generation-Act-Char
results: []
language:
- en
inference:
parameters:
max_length: 128
widget:
- text: >-
I was skating on the outdoor ice pond that used to be across the street
from my house. I was not alone, but I did not recognize any of the other
people who were skating around. I went through my whole repertoire of
jumps, spires, and steps-some of which I can do and some of which I'm not
yet sure of. They were all executed flawlessly-some I repeated, some I did
only once. I seemed to know that if I went into competition, I would be
sure of coming in third because there were only three contestants. Up to
that time I hadn't considered it because I hadn't thought I was good
enough, but now since everything was going so well, I decided to enter.
example_title: Dream
t5-base-DreamBank-Generation-Act-Char
This model is a fine-tuned version of DReAMy-lib/t5-base-DreamBank-Generation-NER-Char on the DreamBank dataset. The uploaded model contains the weights of the best-performing model (see table below), tune to annotate a given dream report according to Hall and Van de Castle the Activity feature
Training procedure
The model is trained end-to-end using a text2text solution to annotate dream reports following the Activity feature
from the Hall and Van de Castle scoring framework. Given a report, the model generates texts of the form
[(initialiser : activity type : receiver)]. For those cases where initialiser and receiver are the same
entity, the output will follow the [(initialiser : alone : activity type)] setting.
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 49 | 0.4061 | 0.3684 | 0.2537 | 0.3495 | 0.3484 |
| No log | 2.0 | 98 | 0.3563 | 0.4151 | 0.3185 | 0.4043 | 0.4030 |
| No log | 3.0 | 147 | 0.3005 | 0.4456 | 0.3588 | 0.4294 | 0.4281 |
| No log | 4.0 | 196 | 0.2693 | 0.4743 | 0.3903 | 0.4586 | 0.4574 |
| No log | 5.0 | 245 | 0.2627 | 0.4751 | 0.3939 | 0.4564 | 0.4549 |
| No log | 6.0 | 294 | 0.2739 | 0.4744 | 0.3920 | 0.4612 | 0.4596 |
| No log | 7.0 | 343 | 0.2733 | 0.4702 | 0.3940 | 0.4557 | 0.4549 |
| No log | 8.0 | 392 | 0.2861 | 0.4739 | 0.3950 | 0.4614 | 0.4608 |
| No log | 9.0 | 441 | 0.3115 | 0.4645 | 0.3868 | 0.4524 | 0.4517 |
| No log | 10.0 | 490 | 0.3212 | 0.4655 | 0.3886 | 0.4524 | 0.4518 |
Framework versions
- Transformers 4.25.1
- Pytorch 1.12.1
- Datasets 2.5.1
- Tokenizers 0.12.1