license: apache-2.0
tags:
- generated_from_trainer
- relation-extraction
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 1
- text: >-
I was talking on the telephone to the father of an old friend of mine
(boy, 21 years old). We were discussing the party the Saturday night
before to which I had invited his son as a guest. I asked him if his son
had a good time at the party. He told me not to tell his son that he had
told me, but that he had had a good time, except he was a little surprised
that I had acted the way I did.
example_title: Dream 2
- text: I was walking alone with my dog in a forest.
example_title: Dream 3
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
Model description
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 : none) setting.
Training procedure
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 | 0.3674 | 0.4008 | 0.3122 | 0.3821 | 0.3812 |
| No log | 2.0 | 98 | 0.3200 | 0.4240 | 0.3433 | 0.4130 | 0.4121 |
| No log | 3.0 | 147 | 0.2845 | 0.4591 | 0.3883 | 0.4459 | 0.4455 |
| No log | 4.0 | 196 | 0.2508 | 0.4614 | 0.3930 | 0.4504 | 0.4497 |
| No log | 5.0 | 245 | 0.2632 | 0.4614 | 0.3929 | 0.4467 | 0.4459 |
| No log | 6.0 | 294 | 0.2688 | 0.4706 | 0.4036 | 0.4537 | 0.4534 |
| No log | 7.0 | 343 | 0.2790 | 0.4682 | 0.4043 | 0.4559 | 0.4556 |
| No log | 8.0 | 392 | 0.2895 | 0.4670 | 0.3972 | 0.4529 | 0.4534 |
| No log | 9.0 | 441 | 0.3058 | 0.4708 | 0.4040 | 0.4576 | 0.4572 |
| No log | 10.0 | 490 | 0.3169 | 0.4690 | 0.4001 | 0.4547 | 0.4544 |
Framework versions
- Transformers 4.25.1
- Pytorch 1.12.1
- Datasets 2.5.1
- Tokenizers 0.12.1