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ontocord/
riverbed

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These are basic classifiers and a BM25 index of Wikipedia used for data tooling research. Using kenhktsui/llm-data-textbook-quality-fasttext-classifer-v1's classifier (MIT) and TurkuNLP's register classifiers.

import fasttext, os
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/kenhktsui/llm-data-textbook-quality-fasttext-classifer-v1/resolve/main/model_textbook_quality.bin")
    os.system("wget https://huggingface.co/ontocord/riverbed/resolve/main/expert_classify.ftz")

### 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

This includes a a small whoosh search index of wikidata useful for background knowledge for LLMs.

installation:

import

if not os.path.exists("./wikidata_bm25_whoosh"):
  os.system("git clone https://huggingface.co/ontocord/riverbed")
  os.system("pip install -q whoosh")
import whoosh.index as whoosh_index
from whoosh.qparser import QueryParser
from whoosh.analysis import StemmingAnalyzer, Filter
class MyFilter(Filter):
  def __call__(self, tokens):

    for t in tokens:
        t.text = t.text.lower()
        if len(t.text) > 5:
          yield t
          t.text = t.text[:5]
        yield t

try:
  if qp is None: assert False
except:
  bm25_dir = "./riverbed"
  index = whoosh_index.open_dir(bm25_dir)
  searcher = index.searcher()
  qp = QueryParser("content", schema=index.schema)