README.md

metadata

license: mit
language:
  - en
size_categories:
  - 100K<n<1M
task_categories:
  - question-answering
tags:
  - finance
  - earnings-calls
  - pit
  - sft
configs:
  - config_name: '202112'
    data_files:
      - split: train
        path: 202112/sft_train.jsonl
      - split: validation
        path: 202112/sft_val.jsonl
      - split: test
        path: 202112/sft_test.jsonl
      - split: benchmark
        path: 202112/benchmark_1k.jsonl
  - config_name: '202212'
    data_files:
      - split: train
        path: 202212/sft_train.jsonl
      - split: validation
        path: 202212/sft_val.jsonl
      - split: test
        path: 202212/sft_test.jsonl
      - split: benchmark
        path: 202212/benchmark_1k.jsonl

Earnings-Call QA dataset for PIT-4B-FT SFT

Supervised fine-tuning mixture for the PIT (Point-in-Time) line of language models, derived from US public-company earnings-call transcripts. Built to fine-tune the Diamegs/PIT-4B-FT-* snapshots while respecting PIT chronological discipline — no transcript dated after the base model's knowledge cutoff is used in training.

Available snapshots

Each snapshot has its own chronological splits keyed to the base model's knowledge cutoff:

• 202112 — for base model Diamegs/PIT-4B-FT-202112
• 202212 — for base model Diamegs/PIT-4B-FT-202212

Load a specific snapshot with the standard Datasets API:

from datasets import load_dataset
ds = load_dataset("jdecim/pit-earnings-call-qa", "202212", split="train")

Snapshot 202212 — splits

Split	Rows	Date range
train	189362	transcript_date < 2022
validation	16778	transcript_date = 2022
test	16900	transcript_date >= 2023
benchmark	1000	held-out subset of test, balanced across buckets

Four QA buckets (same construction across snapshots)

• forward_synthetic — questions generated from the prepared remarks by an LLM (Qwen2.5-32B-Instruct-GPTQ-Int4), with answer = extracted evidence span. The LLM sees an anonymised version of the remarks ([COMPANY], [TICKER] placeholders) to remove generator lookahead, but the final training row uses the un-anonymised remarks — the real company name is restored before the row enters the mixture. PIT-4B therefore trains on the natural transcript text, never on placeholder tokens.
• forward_natural — actual analyst question from Q&A, paired with embedding-selected paragraphs from the prepared remarks as context, answer = echo-stripped management response. No anonymisation in the pipeline (the analyst question came from the transcript itself, not from an LLM).
• inverse_natural — given a management response, generate the question (uses template rotation: "What was the analyst asking about?", …). Trains the model to ground responses back to questions. No anonymisation.
• unanswerable — questions generated by an LLM (on anonymised remarks) that cannot be answered from the transcript; answer is a fixed refusal string. Training context is the un-anonymised prepared remarks. Trains the model to abstain.

Bucket distribution in 202212/train:

• forward_synthetic: 141665 (74.8%)
• unanswerable: 17992 (9.5%)
• forward_natural: 16776 (8.9%)
• inverse_natural: 12929 (6.8%)

Format

Each snapshot subfolder contains two parallel formats per split:

• sft_*.jsonl — flat record: transcript_id, transcript_date, source_type, context, question, answer, question_type, n_words_total
• messages_*.jsonl — chat-format: id, training_type, messages=[{"role":"user",...}, {"role":"assistant",...}], source, bucket

The chat format is what the training script consumes; the flat format is for inspection / dataset analytics. Only sft_*.jsonl, benchmark_1k.jsonl, and grpo_subset.jsonl are published — the messages_*.jsonl files are derivable from them via the training pipeline's renderer.

PIT discipline

PIT-4B-FT snapshots are pretrained on a chronologically-filtered corpus to remove future knowledge by construction. We extend that discipline to fine-tuning:

• Train uses only transcripts dated before the snapshot's cutoff year.
• Val is the cutoff year; test is strictly after.
• Splits are chronological — never random.

The generator LLM that produced forward_synthetic questions has lookahead bias by itself, but it generates only questions + evidence pointers, never answers; and the prepared-remarks input was anonymised before generation. So each produced row carries no future information.

Trained model

See jdecim/SFT_202212-earnings-sft for the adapter trained on snapshot 202212.

License

MIT.