To generate 10 random sentences starting from an empty string on a CUDA device:
from transformers import pipeline, set_seed
generator = pipeline('text-generation', model='agentlans/pythia-14m-finewebedu-sentences', device='cuda')
set_seed(1234)
results = generator("", max_length=100, num_return_sequences=10, do_sample=True)
for x in results:
print(x['generated_text'])
Output:
The main outcome is that the group is associated with other domains.
If you're planning to get a long-term answer, you can check the link "watch" and see that you can change your website.
They are very difficult to make it easy to understand how it works as a healthy.
In the most cases, the prevalence of DTP is reduced from 5-HT1.
It is a significant difference between the risk of injury, and there is no need to be a single complication.
The time of taking too high on the scale of the region is to begin, with a bit other type of view to the whole system.
The total cost of your daily distribution is $24. The overall number of children is 0.5 times is 50.
The more difficult time is to learn the basics of the work, but it is important to do the same job.
It is now on the other hand, however, in the middle of the 19th century, the country must follow the law of the country and alliances between the countries and communities.
This is why it is hard to do this.
Intended uses & limitations
For generating short lines of English text
Could be useful for
data augmentation
creative inspiration
entertainment
CAPTCHA
Can be further finetuned on other data such as:
prompts
famous quotes
news headlines
blog post titles
Limitations include:
Not guaranteed to make sensible, coherent, or grammatically correct sentences
No regard for accuracy or truthfulness whatsoever
It's a bunch of words from a probability model, what do you expect?
The following hyperparameters were used during training:
learning_rate: 5e-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 15.0
Framework versions
Transformers 4.44.2
Pytorch 2.2.2+cu121
Datasets 2.18.0
Tokenizers 0.19.1
Runs of agentlans pythia-14m-finewebedu-sentences on huggingface.co
21
Total runs
1
24-hour runs
1
3-day runs
4
7-day runs
10
30-day runs
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