A Transformer-based SocialNLP toolkit for Farcaster
Project description
FarGlot
A Transformer-based SocialNLP toolkit for Farcaster.
Installation
pip install farglot
Examples
from farglot import CastAnalyzer
sentiment_analyzer=CastAnalyzer.sequence_analzyer_from_model_name(
hub_address="nemes.farcaster.xyz:2283",
model_name="pysentimiento/robertuito-sentiment-analysis"
)
sentiment_analyzer.predict_cast(fid=2, hash_hex="0bcdcbf006ec22b79f37f2cf2a09c33413883937")
# {'NEG': 0.051998768001794815, 'NEU': 0.22470703721046448, 'POS': 0.7232941389083862}
sentiment_analyzer.predict_casts_by_fid(fid=2)
# {'NEG': 0.03734538331627846, 'NEU': 0.505352795124054, 'POS': 0.4573018550872803}
Generate a Training Corpus from a Hub
Install the FarGlot CLI
pip install "farglot[cli]"
Define Training Set Classifier(s)
{
"name": "labels",
"default_value": 1 // optional
}
For multi-label classfication:
[
{
"name": "class_one",
"default_value": 1 // optional
},
{
"name": "class_two",
"default_value": 2 // optional
},
{
"name": "class_three",
"default_value": 3 // optional
}
]
Usage
farglot init
farglot set-classifers-path /path/to/class_configs.json
farglot set-hub-db-path /path/to/.rocks/rocks.hub._default
farglot new-training-set --out ./data/training-set.csv
Tuning
TODO: Example of fine-tuning and uploading dataset and model to Hugging Face
Tuning Resources
Not sure how to where to start? Check out the following blog posts on tuning an LLM:
This largely is largely adapted off of pysentimiento.
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