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Model: rroshann/sec-sentiment-sft-deepseek-14b Source: Original Platform
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---
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license: mit
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base_model: deepseek-ai/DeepSeek-R1-Distill-Qwen-14B
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pipeline_tag: text-generation
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language:
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- en
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tags:
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- finance
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- sec-filings
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- sentiment-analysis
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- qlora
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- sft
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- deepseek
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- vanderbilt-dsi
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library_name: transformers
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---
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# sec-sentiment-sft-deepseek-14b
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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).
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Produced as part of the AllianceBernstein × Vanderbilt DSI capstone project, Spring 2026.
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- **Paper / Technical Report:** [`TECHNICAL_REPORT.md`](https://github.com/WanlinTu/NLP-Project/blob/main/technical_report/TECHNICAL_REPORT.md)
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- **Code:** [github.com/WanlinTu/NLP-Project](https://github.com/WanlinTu/NLP-Project)
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- **Companion model (further RL-aligned):** [`rroshann/sec-sentiment-sftgrpo-deepseek-14b`](https://huggingface.co/rroshann/sec-sentiment-sftgrpo-deepseek-14b)
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* * *
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## Model Details
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| | |
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|---|---|
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| **Architecture** | DeepSeek-R1-Distill-Qwen-14B (dense decoder-only, 14B params) |
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| **Fine-tune method** | QLoRA (NF4 4-bit base + LoRA adapter), merged to a single fp16/bf16 checkpoint |
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| **LoRA rank / alpha / dropout** | 64 / 128 / 0.05 |
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| **Target modules (7)** | `q_proj`, `k_proj`, `v_proj`, `o_proj`, `gate_proj`, `up_proj`, `down_proj` |
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| **Trainable parameter fraction** | ~1.3% of base |
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| **Training hardware** | 1× A100 40GB (Vanderbilt ACCRE) |
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| **Precision** | bf16 mixed |
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| **Checkpoint format** | Merged safetensors (6 shards, 28 GB total) |
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## Intended Uses
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**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.
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**Out of scope.** This is **not** a general-purpose assistant. Do not use it for:
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- Open-ended chat or instruction-following
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- Stock-price prediction, trading signals on a single-factor basis
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- Sentiment analysis outside the U.S. industrials sector or outside SEC-filing prose
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- Downstream applications without the cohort-level aggregation and portfolio-level validation described in the technical report
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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).
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## Training Data
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- **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).
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- **Annotation pipeline:** two-stage weak-to-strong labeling:
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1. Base DeepSeek-R1-Distill-Qwen-14B produces a first-pass 5-class label per factor.
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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).
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- **Tail densification:** +217 samples from two "extreme" chunks targeting known very-negative and very-positive filings (bankruptcy, major contract wins, restructuring).
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- **Final dataset size:** 5,217 samples.
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- **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.
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## Training Procedure
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| Parameter | Value |
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|---|---|
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| Epochs | 3 |
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| Steps | 783 |
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| Learning rate | 2e-4, cosine schedule, 5% warmup |
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| Effective batch size | 16 (2 per-device × 8 grad accumulation) |
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| Optimizer | paged AdamW 8-bit |
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| Max sequence length | 2048 tokens |
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| Quantization | NF4 (double-quant) on base, adapter in bf16 |
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| Final training loss | 0.08 (from 1.55 start) |
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## Evaluation
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**Validation accuracy (1,045-sample held-out Opus-labeled val set):** 73.3%
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**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):
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| Metric | Base | SFT (this model) |
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|---|---|---|
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| Macro F1 | 0.160 | 0.174 |
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| Quadratic Weighted Kappa (QWK) | 0.017 | 0.027 |
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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.
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## Usage
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### Direct inference via vLLM (recommended)
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```bash
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vllm serve rroshann/sec-sentiment-sft-deepseek-14b \
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--dtype bfloat16 \
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--gpu-memory-utilization 0.90 \
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--port 8000 \
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--max-model-len 2048
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```
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Query with any OpenAI-compatible client:
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```python
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from openai import OpenAI
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client = OpenAI(base_url="http://127.0.0.1:8000/v1", api_key="local")
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response = client.chat.completions.create(
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model="rroshann/sec-sentiment-sft-deepseek-14b",
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messages=[{
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"role": "user",
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"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}."
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}],
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temperature=0.0,
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max_tokens=512,
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)
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print(response.choices[0].message.content)
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```
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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.
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### Direct inference via `transformers`
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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import torch
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model_id = "rroshann/sec-sentiment-sft-deepseek-14b"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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torch_dtype=torch.bfloat16,
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device_map="auto",
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)
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messages = [{"role": "user", "content": "<your factor summary + instructions>"}]
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input_ids = tokenizer.apply_chat_template(
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messages,
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return_tensors="pt",
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add_generation_prompt=True,
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).to(model.device)
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outputs = model.generate(
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input_ids,
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max_new_tokens=512,
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do_sample=False, # greedy
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)
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print(tokenizer.decode(outputs[0, input_ids.shape[-1]:], skip_special_tokens=True))
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```
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## Limitations & Biases
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- **Universe specificity.** Trained on 80 U.S. industrials tickers; will underperform on other sectors (tech, finance, healthcare) where the factor taxonomy is calibrated differently.
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- **Single-factor accuracy near chance on return labels.** See Intended Uses. Deploy only with the cohort-aggregation + validity-gate protocol from the technical report.
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- **Single-seed training.** No variance estimate across retraining runs; expected val-accuracy drift of ± 0.5 pp on re-runs with a different seed.
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- **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.
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- **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.
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- **8-K filings excluded.** Event-driven filings break the 60-question taxonomy; model has not been trained on them.
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- **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).
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## Ethical Considerations
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- 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.
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- Predictions are for **research and reproducibility** of the capstone results. Not investment advice. Not audited for deployment in any regulated context.
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- SEC filings are U.S. public-domain government documents (EDGAR). No PII.
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## Citation
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```bibtex
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@techreport{siddartha2026reasoningaugmented,
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title = {Reasoning-Augmented Factor Extraction:
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Enhancing SEC Sentiment Signals through Reinforcement Learning},
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author = {Siddartha, Roshan and Tu, Maggie and Butskhrikidze, Luka},
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year = {2026},
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month = {April},
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institution = {Vanderbilt University Data Science Institute},
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note = {AllianceBernstein × Vanderbilt DSI Capstone. Course:
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NLP for Asset Management. Instructor: Che Guan.}
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}
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```
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## License & Acknowledgements
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- **Model license:** MIT (matches upstream DeepSeek-R1-Distill-Qwen-14B).
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- 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.
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- Training labels generated via the Anthropic API (Claude Opus family).
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- Compute provided by Vanderbilt University ACCRE (DGX A100).
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- Project advised by Che Guan, Vanderbilt Data Science Institute.
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## Companion Model
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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):
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→ [`rroshann/sec-sentiment-sftgrpo-deepseek-14b`](https://huggingface.co/rroshann/sec-sentiment-sftgrpo-deepseek-14b)
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{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{% set ns = namespace(is_first=false, is_tool=false, is_output_first=true, system_prompt='') %}{%- for message in messages %}{%- if message['role'] == 'system' %}{% set ns.system_prompt = message['content'] %}{%- endif %}{%- endfor %}{{bos_token}}{{ns.system_prompt}}{%- for message in messages %}{%- if message['role'] == 'user' %}{%- set ns.is_tool = false -%}{{'<｜User｜>' + message['content']}}{%- endif %}{%- if message['role'] == 'assistant' and message['content'] is none %}{%- set ns.is_tool = false -%}{%- for tool in message['tool_calls']%}{%- if not ns.is_first %}{{'<｜Assistant｜><｜tool▁calls▁begin｜><｜tool▁call▁begin｜>' + tool['type'] + '<｜tool▁sep｜>' + tool['function']['name'] + '\n' + '```json' + '\n' + tool['function']['arguments'] + '\n' + '```' + '<｜tool▁call▁end｜>'}}{%- set ns.is_first = true -%}{%- else %}{{'\n' + '<｜tool▁call▁begin｜>' + tool['type'] + '<｜tool▁sep｜>' + tool['function']['name'] + '\n' + '```json' + '\n' + tool['function']['arguments'] + '\n' + '```' + '<｜tool▁call▁end｜>'}}{{'<｜tool▁calls▁end｜><｜end▁of▁sentence｜>'}}{%- endif %}{%- endfor %}{%- endif %}{%- if message['role'] == 'assistant' and message['content'] is not none %}{%- if ns.is_tool %}{{'<｜tool▁outputs▁end｜>' + message['content'] + '<｜end▁of▁sentence｜>'}}{%- set ns.is_tool = false -%}{%- else %}{% set content = message['content'] %}{% if '</think>' in content %}{% set content = content.split('</think>')[-1] %}{% endif %}{{'<｜Assistant｜>' + content + '<｜end▁of▁sentence｜>'}}{%- endif %}{%- endif %}{%- if message['role'] == 'tool' %}{%- set ns.is_tool = true -%}{%- if ns.is_output_first %}{{'<｜tool▁outputs▁begin｜><｜tool▁output▁begin｜>' + message['content'] + '<｜tool▁output▁end｜>'}}{%- set ns.is_output_first = false %}{%- else %}{{'\n<｜tool▁output▁begin｜>' + message['content'] + '<｜tool▁output▁end｜>'}}{%- endif %}{%- endif %}{%- endfor -%}{% if ns.is_tool %}{{'<｜tool▁outputs▁end｜>'}}{% endif %}{% if add_generation_prompt and not ns.is_tool %}{{'<｜Assistant｜><think>\n'}}{% endif %}
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{
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"architectures": [
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"Qwen2ForCausalLM"
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],
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"attention_dropout": 0.0,
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"bos_token_id": 151643,
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"dtype": "float16",
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"eos_token_id": 151643,
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"hidden_act": "silu",
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"hidden_size": 5120,
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"initializer_range": 0.02,
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"intermediate_size": 13824,
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"layer_types": [
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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|
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"full_attention",
|
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"full_attention",
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"full_attention",
|
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"full_attention"
|
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],
|
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"max_position_embeddings": 131072,
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"max_window_layers": 48,
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"model_type": "qwen2",
|
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"num_attention_heads": 40,
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"num_hidden_layers": 48,
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"num_key_value_heads": 8,
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"pad_token_id": null,
|
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"rms_norm_eps": 1e-05,
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"rope_parameters": {
|
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"rope_theta": 1000000.0,
|
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"rope_type": "default"
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},
|
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"sliding_window": null,
|
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"tie_word_embeddings": false,
|
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"transformers_version": "5.3.0",
|
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"use_cache": true,
|
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"use_sliding_window": false,
|
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"vocab_size": 152064
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}
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{
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"_from_model_config": true,
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"bos_token_id": 151646,
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"do_sample": true,
|
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"eos_token_id": 151643,
|
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"temperature": 0.6,
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"top_p": 0.95,
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"transformers_version": "5.3.0"
|
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}
|
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"model.layers.8.self_attn.v_proj.bias": "model-00002-of-00006.safetensors",
|
||||
"model.layers.8.self_attn.v_proj.weight": "model-00002-of-00006.safetensors",
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||||
"model.layers.9.input_layernorm.weight": "model-00002-of-00006.safetensors",
|
||||
"model.layers.9.mlp.down_proj.weight": "model-00002-of-00006.safetensors",
|
||||
"model.layers.9.mlp.gate_proj.weight": "model-00002-of-00006.safetensors",
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||||
"model.layers.9.mlp.up_proj.weight": "model-00002-of-00006.safetensors",
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"model.layers.9.post_attention_layernorm.weight": "model-00002-of-00006.safetensors",
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||||
"model.layers.9.self_attn.k_proj.bias": "model-00002-of-00006.safetensors",
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"model.layers.9.self_attn.k_proj.weight": "model-00002-of-00006.safetensors",
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||||
"model.layers.9.self_attn.o_proj.weight": "model-00002-of-00006.safetensors",
|
||||
"model.layers.9.self_attn.q_proj.bias": "model-00002-of-00006.safetensors",
|
||||
"model.layers.9.self_attn.q_proj.weight": "model-00002-of-00006.safetensors",
|
||||
"model.layers.9.self_attn.v_proj.bias": "model-00002-of-00006.safetensors",
|
||||
"model.layers.9.self_attn.v_proj.weight": "model-00002-of-00006.safetensors",
|
||||
"model.norm.weight": "model-00006-of-00006.safetensors"
|
||||
}
|
||||
}
|
||||
3
tokenizer.json
Normal file
3
tokenizer.json
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:322664cdc3082b6eba003af5228a77ca1d7936d402e584ecde8f15d3d98bdb72
|
||||
size 11421911
|
||||
13
tokenizer_config.json
Normal file
13
tokenizer_config.json
Normal file
@@ -0,0 +1,13 @@
|
||||
{
|
||||
"backend": "tokenizers",
|
||||
"bos_token": "<｜begin▁of▁sentence｜>",
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "<｜end▁of▁sentence｜>",
|
||||
"is_local": false,
|
||||
"legacy": true,
|
||||
"model_max_length": 16384,
|
||||
"pad_token": "<｜end▁of▁sentence｜>",
|
||||
"sp_model_kwargs": {},
|
||||
"tokenizer_class": "TokenizersBackend",
|
||||
"unk_token": null
|
||||
}
|
||||
Reference in New Issue
Block a user