GPT Agents
SBIR
Book
JuryRoom
GPT Agents
Book
SBIR
GPT Agents
SBIR
Mail taxes
SBIR
Book
GPT Agents
Print and mail taxes today
How many data points is a prompt worth?
SBIR
Book
GPT Agents
Two perspectives on large language model (LLM) ethics
On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? 🦜
Book
GPT Agents
SBIR
JuryRoom
Need to agree to re-review
GPT Agents
SBIR
GPT Agents
There are also some countries that are very far away from the United States. Here's a short list, starting with the most distant, separated by commas:
SBIR
Happy end-of-Passover, Easter!
Playing with the GPT mapping, and I’ve gotten queries running with POS processing. Here’s the prompt:
"A list of the countries that are nearest the United States, separated by comma:"
Here’s the response:
Canada, Mexico, Bahamas, Dominican Republic, Haiti, Jamaica, Cuba, Trinidad and Tobago, Puerto Rico, Barbados, Antigua and Barbuda, Saint Lucia, Saint Vincent and the Grenadines, Grenada, DominAnd here it is processed by Flair:
{'text': 'Canada', 'tag': 'NNP'}
{'text': ',', 'tag': ','}
{'text': 'Mexico', 'tag': 'NNP'}
{'text': ',', 'tag': ','}
{'text': 'Bahamas', 'tag': 'NNP'}
{'text': ',', 'tag': ','}
{'text': 'Dominican', 'tag': 'NNP'}
{'text': 'Republic', 'tag': 'NNP'}
{'text': ',', 'tag': ','}
{'text': 'Haiti', 'tag': 'NNP'}
{'text': ',', 'tag': ','}
{'text': 'Jamaica', 'tag': 'NNP'}
{'text': ',', 'tag': ','}
{'text': 'Cuba', 'tag': 'NNP'}
{'text': ',', 'tag': ','}
{'text': 'Trinidad', 'tag': 'NNP'}
{'text': 'and', 'tag': 'CC'}
{'text': 'Tobago', 'tag': 'NNP'}
{'text': ',', 'tag': ','}
{'text': 'Puerto', 'tag': 'NNP'}
{'text': 'Rico', 'tag': 'NNP'}
{'text': ',', 'tag': ','}
{'text': 'Barbados', 'tag': 'NNP'}
{'text': ',', 'tag': ','}
{'text': 'Antigua', 'tag': 'NNP'}
{'text': 'and', 'tag': 'CC'}
{'text': 'Barbuda', 'tag': 'NNP'}
{'text': ',', 'tag': ','}
{'text': 'Saint', 'tag': 'NNP'}
{'text': 'Lucia', 'tag': 'NNP'}
{'text': ',', 'tag': ','}
{'text': 'Saint', 'tag': 'NNP'}
{'text': 'Vincent', 'tag': 'NNP'}
{'text': 'and', 'tag': 'CC'}
{'text': 'the', 'tag': 'DT'}
{'text': 'Grenadines', 'tag': 'NNPS'}
{'text': ',', 'tag': ','}
{'text': 'Grenada', 'tag': 'NNP'}
{'text': ',', 'tag': ','}
{'text': 'Domin', 'tag': 'NNP'}I am very excited!
I think milling = fashion
GPT Agents
select count(*) as count, probe from table_output where experiment_id = 89 and tag = 'raw' and sent_label = 'NEGATIVE' group by probe order by probe;
SBIR
Book
Exploring the effects of algorithm-driven news sources on political behavior and polarization
GPT Agents
SBIR
Cool thing for the day!
GPT-3 (actually GPT-Neo) is available on Huggingface: huggingface.co/EleutherAI/gpt-neo-1.3B
GPT Agents
python run_clm.py \
--model_name_or_path gpt2 \
--train_file path_to_train_file \
--validation_file path_to_validation_file \
--do_train \
--do_eval \
--output_dir /tmp/test-clm
from transformers import BertForSequenceClassification, Trainer, TrainingArguments
model = BertForSequenceClassification.from_pretrained("bert-large-uncased")
training_args = TrainingArguments(
output_dir='./results', # output directory
num_train_epochs=3, # total # of training epochs
per_device_train_batch_size=16, # batch size per device during training
per_device_eval_batch_size=64, # batch size for evaluation
warmup_steps=500, # number of warmup steps for learning rate scheduler
weight_decay=0.01, # strength of weight decay
logging_dir='./logs', # directory for storing logs
)
trainer = Trainer(
model=model, # the instantiated 🤗 Transformers model to be trained
args=training_args, # training arguments, defined above
train_dataset=train_dataset, # training dataset
eval_dataset=test_dataset # evaluation dataset
)SBIR
GPT Agents
import flair
from flair.models import TextClassifier
flair_sentiment = TextClassifier.load('en-sentiment')
text="Avengers: Infinity War is a giant battle for which directors Anthony and Joe Russo have given us touches of JRR Tolkien’s Return of the King and JK Rowling’s Harry Potter and the Deathly Hallows. The film delivers the sugar-rush of spectacle and some very amusing one-liners."
sentence=flair.data.Sentence(text)
flair_sentiment.predict(sentence)
total_sentiment = sentence.labels
print(total_sentiment)
Output:
[POSITIVE (0.9994151592254639)]
SBIR
IT’s the end of the month, so it’s time for these two charts again. i think we’re seeing the Vaccines starting to have and effect? At least Switzerland seems to have gotten its second wave under control. Italy though…
And here’s the USA. Georgia is over two times worse than the UK. Think about that.
SBIR
GPT-Agents
Vaccine today (hopefully)! Here’s Maryland one year ago:
And here we are today:
What a terrible year.
GPT Agents
