metadata
license: apache-2.0
These are basic classifiers and a BM25 index of Wikipedia used for data tooling research.
import fasttext
if not os.path.exists("expert_classify.ftz"):
os.system("wget http://dl.turkunlp.org/register-labeling-model/fasttext_model.bin")
os.system("wget https://huggingface.co/ontocord/riverbed/resolve/main/rj_model.bin")
os.system("wget https://huggingface.co/ontocord/riverbed/resolve/main/expert_classify.ftz")
os.system("wget https://huggingface.co/kenhktsui/llm-data-textbook-quality-fasttext-classifer-v1/resolve/main/model_textbook_quality.bin"
### red pajama filter. pred_label "__label__wiki" is data we do not wish to keep.
red_pajama_model = fasttext.load_model("rj_model.bin")
(pred_label, pred_prob) = red_pajama_model.predict(text)
if pred_label == "__label__cc":
pred_prob = 1 - pred_prob
### turkunlp registry labeler: https://github.com/TurkuNLP/register-labeling
domain_model = fasttext.load_model("fasttext_model.bin")
(pred_label, pred_prob) = domain_model.predict(text)
### Pile domain such as github, arxiv, etc.
pile_model = fasttext.load_model("expert_classify.ftz")
(pred_label, pred_prob) = pile_model.predict(text)
### Textbook quality - e.g., textbooks are all you need
textbook_model = fasttext.load_model("model_textbook_quality.bin")
(pred_label, pred_prob) = pile_model.predict(text)
See the files here: https://huggingface.co/ontocord/riverbed/tree/main