--- license: mit base_model: deepseek-ai/DeepSeek-R1-Distill-Qwen-14B pipeline_tag: text-generation language: - en tags: - finance - sec-filings - sentiment-analysis - qlora - sft - deepseek - vanderbilt-dsi library_name: transformers --- # sec-sentiment-sft-deepseek-14b Supervised fine-tune of [`deepseek-ai/DeepSeek-R1-Distill-Qwen-14B`](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-14B) for 5-class sentiment classification of thematic factors extracted from U.S. industrials SEC filings (10-K, 10-Q). Produced as part of the AllianceBernstein × Vanderbilt DSI capstone project, Spring 2026. - **Paper / Technical Report:** [`TECHNICAL_REPORT.md`](https://github.com/WanlinTu/NLP-Project/blob/main/technical_report/TECHNICAL_REPORT.md) - **Code:** [github.com/WanlinTu/NLP-Project](https://github.com/WanlinTu/NLP-Project) - **Companion model (further RL-aligned):** [`rroshann/sec-sentiment-sftgrpo-deepseek-14b`](https://huggingface.co/rroshann/sec-sentiment-sftgrpo-deepseek-14b) * * * ## Model Details | | | |---|---| | **Architecture** | DeepSeek-R1-Distill-Qwen-14B (dense decoder-only, 14B params) | | **Fine-tune method** | QLoRA (NF4 4-bit base + LoRA adapter), merged to a single fp16/bf16 checkpoint | | **LoRA rank / alpha / dropout** | 64 / 128 / 0.05 | | **Target modules (7)** | `q_proj`, `k_proj`, `v_proj`, `o_proj`, `gate_proj`, `up_proj`, `down_proj` | | **Trainable parameter fraction** | ~1.3% of base | | **Training hardware** | 1× A100 40GB (Vanderbilt ACCRE) | | **Precision** | bf16 mixed | | **Checkpoint format** | Merged safetensors (6 shards, 28 GB total) | ## Intended Uses **In scope.** Financial-materiality sentiment classification of individual factor summaries extracted from 10-K / 10-Q filings. Input = a factor-level summary paragraph. Output = one of five ordinal labels (`very_negative`, `negative`, `neutral`, `positive`, `very_positive`) plus a natural-language rationale and a confidence score. **Out of scope.** This is **not** a general-purpose assistant. Do not use it for: - Open-ended chat or instruction-following - Stock-price prediction, trading signals on a single-factor basis - Sentiment analysis outside the U.S. industrials sector or outside SEC-filing prose - Downstream applications without the cohort-level aggregation and portfolio-level validation described in the technical report Per-sample accuracy is near the 5-class uniform baseline (~20%) on realized-return-quintile gold labels — by design. The model's value comes from the **cohort-level ordinal shape** of predictions across a pre-registered backtest panel (see technical report §11). ## Training Data - **Source corpus:** 67,741 thematic factors extracted from 2,441 10-K and 10-Q filings (80 U.S. industrials tickers, 2015-01 → 2025-06). - **Annotation pipeline:** two-stage weak-to-strong labeling: 1. Base DeepSeek-R1-Distill-Qwen-14B produces a first-pass 5-class label per factor. 2. Claude Opus re-labels each factor against a financial-materiality rubric. 45.6% of base labels change (disagreement rate between two LLMs — not a human-validated correction rate). - **Tail densification:** +217 samples from two "extreme" chunks targeting known very-negative and very-positive filings (bankruptcy, major contract wins, restructuring). - **Final dataset size:** 5,217 samples. - **Splits:** 4,172 train / 1,045 validation (factor-level stratified split on the 5-class label, `random_state=42`). **Note:** the split is at the factor level, not the filing level — see technical report §6.4 for the disclosed limitation. ## Training Procedure | Parameter | Value | |---|---| | Epochs | 3 | | Steps | 783 | | Learning rate | 2e-4, cosine schedule, 5% warmup | | Effective batch size | 16 (2 per-device × 8 grad accumulation) | | Optimizer | paged AdamW 8-bit | | Max sequence length | 2048 tokens | | Quantization | NF4 (double-quant) on base, adapter in bf16 | | Final training loss | 0.08 (from 1.55 start) | ## Evaluation **Validation accuracy (1,045-sample held-out Opus-labeled val set):** 73.3% **Classification metrics on the 18,466-factor pre-registered test set** (gold label = filing's next-period realized-return quintile, a fundamentally different and harder target than the Opus-labeled val set): | Metric | Base | SFT (this model) | |---|---|---| | Macro F1 | 0.160 | 0.174 | | Quadratic Weighted Kappa (QWK) | 0.017 | 0.027 | The +1.4 pp F1 gain over base is modest at the sample level; the full portfolio-level story (SFT lifts L/S cohort spread from 2.78% to 4.88% at 21-day horizon) is in the technical report §7.5. ## Usage ### Direct inference via vLLM (recommended) ```bash vllm serve rroshann/sec-sentiment-sft-deepseek-14b \ --dtype bfloat16 \ --gpu-memory-utilization 0.90 \ --port 8000 \ --max-model-len 2048 ``` Query with any OpenAI-compatible client: ```python from openai import OpenAI client = OpenAI(base_url="http://127.0.0.1:8000/v1", api_key="local") response = client.chat.completions.create( model="rroshann/sec-sentiment-sft-deepseek-14b", messages=[{ "role": "user", "content": "Factor: Supply chain pressure from component shortages...\n\nClassify sentiment into one of [very_negative, negative, neutral, positive, very_positive] and return JSON: {label, rationale, confidence}." }], temperature=0.0, max_tokens=512, ) print(response.choices[0].message.content) ``` See `roshan/Actual_code/task_1/03_factor_extraction.py` and `04_sentiment_scoring.py` in the GitHub repo for the exact system prompts and JSON schemas used to produce the 67,741-factor corpus. ### Direct inference via `transformers` ```python from transformers import AutoTokenizer, AutoModelForCausalLM import torch model_id = "rroshann/sec-sentiment-sft-deepseek-14b" tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained( model_id, torch_dtype=torch.bfloat16, device_map="auto", ) messages = [{"role": "user", "content": " "}] input_ids = tokenizer.apply_chat_template( messages, return_tensors="pt", add_generation_prompt=True, ).to(model.device) outputs = model.generate( input_ids, max_new_tokens=512, do_sample=False, # greedy ) print(tokenizer.decode(outputs[0, input_ids.shape[-1]:], skip_special_tokens=True)) ``` ## Limitations & Biases - **Universe specificity.** Trained on 80 U.S. industrials tickers; will underperform on other sectors (tech, finance, healthcare) where the factor taxonomy is calibrated differently. - **Single-factor accuracy near chance on return labels.** See Intended Uses. Deploy only with the cohort-aggregation + validity-gate protocol from the technical report. - **Single-seed training.** No variance estimate across retraining runs; expected val-accuracy drift of ± 0.5 pp on re-runs with a different seed. - **Factor-level (not filing-level) train/val split.** Factors from the same filing can appear in both splits. Does not affect the downstream test-set metrics because the test set is filing-level and time-ordered (2023–mid-2025), but the 73.3% val accuracy should be read with this in mind. - **Claude-derived labels.** Training labels reflect Claude Opus's financial-materiality rubric, not a human-panel gold standard. Opus-vs-human agreement was not measured. - **8-K filings excluded.** Event-driven filings break the 60-question taxonomy; model has not been trained on them. - **Beta-one signal.** Dollar-neutral portfolios built on this model's predictions have |β| ≈ 2.0 against SPY in backtests — not beta-neutral (see report §13). ## Ethical Considerations - Training labels were generated via the Anthropic API (Claude Opus). Use of Claude outputs to train a model is permitted under Anthropic's Commercial Terms for non-competing, domain-specific applications; this model is a 5-class sentiment classifier for SEC filings, not a general-purpose assistant. - Predictions are for **research and reproducibility** of the capstone results. Not investment advice. Not audited for deployment in any regulated context. - SEC filings are U.S. public-domain government documents (EDGAR). No PII. ## Citation ```bibtex @techreport{siddartha2026reasoningaugmented, title = {Reasoning-Augmented Factor Extraction: Enhancing SEC Sentiment Signals through Reinforcement Learning}, author = {Siddartha, Roshan and Tu, Maggie and Butskhrikidze, Luka}, year = {2026}, month = {April}, institution = {Vanderbilt University Data Science Institute}, note = {AllianceBernstein × Vanderbilt DSI Capstone. Course: NLP for Asset Management. Instructor: Che Guan.} } ``` ## License & Acknowledgements - **Model license:** MIT (matches upstream DeepSeek-R1-Distill-Qwen-14B). - Upstream base model: DeepSeek-AI, released under MIT. See [`deepseek-ai/DeepSeek-R1-Distill-Qwen-14B`](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-14B) for their model card. - Training labels generated via the Anthropic API (Claude Opus family). - Compute provided by Vanderbilt University ACCRE (DGX A100). - Project advised by Che Guan, Vanderbilt Data Science Institute. ## Companion Model The `sft_grpo` variant of this model adds a GRPO alignment stage on top of the SFT checkpoint, using a composite ordinal-plus-anti-neutral reward against realized-return-quintile gold labels. It is the stronger variant on the portfolio-level backtest (L/S cohort spread 8.12% at H=21d vs 4.88% for SFT alone; adding a Self-Consistency Best-of-N decoding overlay at inference time gives a variant we label `sft_grpo_bon` at 8.09% — see technical report §9 and §11.3): → [`rroshann/sec-sentiment-sftgrpo-deepseek-14b`](https://huggingface.co/rroshann/sec-sentiment-sftgrpo-deepseek-14b)
