Initial release: FinBERT LoRA adapter for operational KPI sentiment

README.md

@@ -1,206 +1,146 @@
 ---
 base_model: ProsusAI/finbert
 library_name: peft
+license: apache-2.0
 tags:
-- base_model:adapter:ProsusAI/finbert
+- sentiment-analysis
+- finance
+- operational-metrics
 - lora
 - transformers
+- domain-adaptation
+- bias-correction
 ---
 
-# Model Card for Model ID
+# FinBERT LoRA Adapter for Operational Metrics Sentiment
 
-<!-- Provide a quick summary of what the model is/does. -->
+This repository provides a **LoRA adapter** for `ProsusAI/finbert` that mitigates a domain bias commonly observed in financial sentiment models.
 
 
+## Motivation
 
-## Model Details
+Standard FinBERT models are heavily trained on financial news and reports.  
+As a result, phrases containing words such as **"down"**, **"reduced"**, or **"failure"**  
+are often interpreted as **negative signals**,  
+even when they describe improvements in operational or quality-related metrics.
 
-### Model Description
+However, in manufacturing, operations, and enterprise contexts, statements like:
 
-<!-- Provide a longer summary of what this model is. -->
+> *"Failure rate down 10% QoQ"*
 
+represent **positive operational improvements**.
 
+This adapter reduces that semantic conflict inside the model, without rule-based postprocessing.
 
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-
-## Uses
-
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
-### Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
-
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
-
-## Training Details
-
-### Training Data
-
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
-
-### Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
+---
 
-[More Information Needed]
+## What This Adapter Does
 
-#### Summary
+✅ Classifies **decreases in bad operational metrics** as **Positive**
 
+**Quality / Operations KPIs**
+- defect rate  
+- error rate  
+- failure rate  
+- scrap rate  
+- return rate  
 
+✅ Preserves **negative sentiment** for genuine financial deterioration
 
-## Model Examination [optional]
+- revenue down  
+- profit reduced  
+- sales declined  
 
-<!-- Relevant interpretability work for the model goes here -->
+### Base vs Adapter (sample inference, local run)
 
-[More Information Needed]
+| Text | Base FinBERT | +Adapter (LoRA) |
+|---|---:|---:|
+| Failure rate down 10% QoQ | **Negative (0.9640)** | **Positive (0.9000)** |
+| Defect rate reduced | **Neutral (0.6343)** | **Positive (0.7902)** |
+| Revenue reduced by 20% | **Negative (0.9690)** | **Negative (0.9469)** |
+| The production line was audited last week. | **Neutral (0.5524)** | **Positive (0.6094)** |
 
-## Environmental Impact
+The adapter consistently reclassifies decreases in negative operational KPIs as **Positive**, while preserving **Negative** sentiment for genuine financial deterioration.
 
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+These examples are based on a small set of manually selected sentences and are intended for illustrative comparison.
 
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
 
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
 
-## Technical Specifications [optional]
+## Label Mapping
 
-### Model Architecture and Objective
+0 → positive
+1 → negative
+2 → neutral
 
-[More Information Needed]
+---
 
-### Compute Infrastructure
+## How to Use
 
-[More Information Needed]
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+from peft import PeftModel
+import torch
 
-#### Hardware
+base = "ProsusAI/finbert"
+adapter = "yahoyaho13/finbert-lora-operational-sentiment"
 
-[More Information Needed]
+device = "cuda" if torch.cuda.is_available() else "cpu"
 
-#### Software
+tokenizer = AutoTokenizer.from_pretrained(base)
+base_model = AutoModelForSequenceClassification.from_pretrained(
+    base,
+    num_labels=3,
+    id2label={0: "positive", 1: "negative", 2: "neutral"},
+    label2id={"positive": 0, "negative": 1, "neutral": 2},
+)
 
-[More Information Needed]
+model = PeftModel.from_pretrained(base_model, adapter).to(device).eval()
 
-## Citation [optional]
+text = "Failure rate down 10% QoQ"
+inputs = tokenizer(text, return_tensors="pt").to(device)
 
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+with torch.inference_mode():
+    probs = torch.softmax(model(**inputs).logits, dim=-1)
 
-**BibTeX:**
+print({base_model.config.id2label[i]: round(float(probs[0, i]), 4) for i in range(3)})
 
-[More Information Needed]
 
-**APA:**
+```
 
-[More Information Needed]
+## Training Summary
+Base model: ProsusAI/finbert  
+Fine-tuning method: LoRA (PEFT)  
+Target modules: all-linear  
 
-## Glossary [optional]
+LoRA configuration:
+- r = 16
+- lora_alpha = 64
+- dropout = 0.05
 
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+Dataset size: ~170 short operational / financial statements  
+Hardware: NVIDIA GTX 1060 6GB (local training)
 
-[More Information Needed]
+---
 
-## More Information [optional]
+## Known Limitations
+- Trained on a small, domain-specific dataset.
+- Not intended as a general-purpose financial sentiment replacement.
+- Best suited for short operational or KPI-style sentences.
+- May over-predict **Positive** sentiment for some neutral operational statements due to limited training data.
 
-[More Information Needed]
+---
 
-## Model Card Authors [optional]
+## Intended Use
+- Manufacturing and quality reporting
+- Enterprise KPI commentary
+- Mixed finance/operations text where **rate decreases imply improvement**
 
-[More Information Needed]
+---
 
-## Model Card Contact
+## License
+Apache-2.0 (inherits base model license)
 
-[More Information Needed]
-### Framework versions
+---
 
-- PEFT 0.18.0
+## Author
+- Hugging Face: **yahoyaho13**