| --- |
| 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: |
|
|
| ```python |
| 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`](https://huggingface.co/jdecim/SFT_202212-earnings-sft) for the adapter trained on snapshot `202212`. |
| |
| ## License |
| |
| MIT. |
| <!-- retrigger viewer --> |
| |
