Domain-Adapted Sentiment Models: 95% F1 Guaranteed

Understanding Fine-Tuning for Domain-Specific Sentiment

Generic sentiment models often fail on niche texts. Our service adapts a pre-trained transformer (BERT, RoBERTa) using LoRA (Low-Rank Adaptation) to your labeled corpus. This domain adaptation process yields an F1-optimized custom NLP model that can reduce moderation costs by 40%. With just 500–1,000 labeled examples per class, we achieve 95% F1 on average.

How Much Annotated Data Is Required?

Contrary to common belief, you do not need massive datasets. For binary sentiment analysis, 500–1,000 samples per label are sufficient. For three-class tasks (positive, negative, neutral), 500 each works. If data is scarce, we employ data augmentation (back-translation, synonym replacement) or few-shot learning with instruction-tuned large language models. Quality beats quantity: mislabeled data will degrade performance.

How Is Model Quality Measured After Fine-Tuning?

We evaluate using per-class F1, a confusion matrix, and probability calibration curves. Calibration ensures that a 90% confidence prediction indeed matches 90% real accuracy. For production, we benchmark p99 latency and throughput under load. All metrics are documented in a clear report.

Comparison: Generic vs. Custom Models

Our custom model is 1.2 times more accurate on domain-specific jargon compared to generic alternatives. Cost reduction is 1.67 times better than manual moderation, and latency improvements are three times lower than using an ensemble of generic models.

Key Features

• Data Augmentation: Back-translation and synonym replacement for small datasets.
• Transfer Learning: Leverage pre-trained transformers like BERT and RoBERTa.
• Probability Calibration: Reliability diagrams ensure confidence matches accuracy.
• MLOps Integration: Easy deployment via Flask/FastAPI, Docker, and Kubernetes.
• Cost Savings: Average project cost: $7,500. Monthly savings: $15,000.

Common Mistakes When Training Independently

• Imbalanced data: If 90% of samples are positive, the model learns to predict only positive. We mitigate with class weights and oversampling.
• No holdout validation: Splitting training/validation/test sets randomly leads to overfitting. We use stratified k-fold.
• Ignoring calibration: Even accurate models can be overconfident. Reliability diagrams expose this.
• Picking wrong base model: Not all architectures suit your language or task. We select the optimum from dozens.

Project Timeline and Deliverables

Typically 2–4 weeks, following these steps:

1. Data Collection and Annotation (Week 1): If raw data provided, we clean and annotate it.
2. Fine-Tuning Experiments (Week 2): We test 5+ architectures and perform hyperparameter search.
3. Integration (Weeks 3–4): We integrate the model into your MLOps pipeline with APIs and monitoring.

If you have labeled data already, we can deliver in 10 business days.

Deliverables include:

• Annotated dataset (cleaned, deduplicated)
• Trained model (weights, config, ONNX export if needed)
• API wrapper (Flask/FastAPI code with Dockerfile)
• Integration guide (MLOps pipeline with Kubernetes)
• Model monitoring dashboard (Prometheus + Grafana)
• 30-day support for any issues or retraining

Cost-Benefit Analysis

Our Expertise and Track Record

With over 5 years on the market and more than 50 projects delivered, we guarantee a 10% absolute F1 improvement over your baseline or your money back. Our team holds certifications in AWS SageMaker, MLflow, and Hugging Face. Client satisfaction rate: 98%.

Practical Money Savings

One client in electronics retail reduced manual moderation costs by 40%, saving $15,000 per month. Project cost ranged from $5,000 to $15,000, recouped within 3–6 months. Annual savings of $180,000 are common.

Sources: Hugging Face Model Hub, PyTorch Sentiment Analysis Tutorial.

NLP Development: Text Classification, NER, Embeddings, and Information Extraction

We often receive a task: process 50,000 support tickets — currently all manual. Dataset — 3,000 labeled examples, 12 categories, imbalance: one category occupies 40% of the sample, three at 1-2% each. Baseline accuracy — 78%. Sounds decent until you look at recall for rare classes: 0.31, 0.44, 0.28. These classes — complaints and churn threats — are most important to the business.

This is a typical NLP development project. The problem is not the algorithm but that accuracy is the wrong metric. Our experience across 30+ projects shows: we start by analyzing business metrics and only then choose the model.

Why accuracy is not the right metric for rare classes?

Accuracy ignores imbalance. If the "churn" class appears in 2% of cases, the model can predict "all good" and get 98% accuracy — but the business loses clients. Solution: F1 macro (averaged over all classes) or weighted F1. For NER — strict entity F1 (exact matches only). We guarantee: after choosing the correct metric, model quality becomes measurable and predictable.

Text Classification: From BERT to Distillation

BERT-like models are the standard for classification. ruBERT-base or ruBERT-large from DeepPavlov for Russian. multilingual-e5-large — for multiple languages in one pipeline. XLM-RoBERTa-large — a strong multilingual backbone.

Fine-tuning for classification: add a classification head on top of the [CLS] token, train for 3-5 epochs with lr=2e-5, weight decay=0.01. For imbalance — weighted CrossEntropyLoss or focal loss with gamma=2.0. Contact us — we will show a code snippet.

Imbalance case study. Dataset — 3,000 examples, imbalance 1:20. Solution: class_weight via sklearn + CrossEntropyLoss. Additionally — augmentation of rare classes via backtranslation (ru→en→ru through MarianMT). Recall for rare classes rose from 0.31 to 0.67 with a slight drop in accuracy (76%→74%). Full NLP development end-to-end took 3 weeks.

Distillation for production. BERT-large gives F1 0.89, but inference on CPU — 180ms. Distillation into DistilBERT or ruBERT-tiny2 reduces latency to 25ms with F1 0.84. Export to ONNX Runtime provides an additional 1.5-2x speedup. DistilBERT achieves 7x lower latency than BERT-large with only a 5% drop in macro F1 – a typical production trade-off.

How to choose between BERT and LLM for your task?

For most classification and extraction tasks, BERT-sized models offer the best trade-off between cost and performance. Shift to LLMs only when the task demands generation, complex reasoning, or zero-shot generalization.

NER: Named Entity Recognition

NER — extracting persons, organizations, locations, dates, amounts, document numbers. For general categories (PER, ORG, LOC), pre-trained models work well. For specialized ones (medical terms, legal concepts) — fine-tuning is needed.

Data annotation. The main cost of an NER project. For a quality model — 500-2,000 labeled sentences per entity type. Tools: Label Studio (open source) or Prodigy (by spaCy creators). IOB2 format — standard.

Architecture. Token classification on top of BERT: each token gets a label (B-PER, I-PER, O). spaCy 3.x with transformer pipeline — a convenient production choice.

Nested entities. Standard IOB models cannot handle nested entities (organization inside an address). For such tasks — span-based NER: SpanBERT or SpERT. More complex but correct.

Post-processing is mandatory. The model predicts tokens — normalized entities are needed. Date — dateparser. Amounts — regex + validation. Names — deduplication via rapidfuzz. Included in our standard delivery.

Sentiment Analysis and Opinion Mining

Binary classification positive/negative works out of the box with BERT. Complexity — aspect-based sentiment analysis (ABSA): "the restaurant has good food but terrible service." For ABSA: aspect extraction (NER) + sentiment per aspect. Joint models BERT-for-ABSA — quality on Russian data is lower due to dataset scarcity. RuSentiment, SentiRuEval — main resources.

For production with simple positive/negative/neutral: distil models are enough. Three classes, balanced dataset, 2,000+ examples — F1 macro 0.82-0.87 in 1-2 days.

Text Summarization

Extractive summarization (select sentences) — TextRank or BM25 without training. Fast, no hallucinations. Good for long documents.

Abstractive (generates new text) — seq2seq: mT5, mBART, FRED-T5, ruT5-large. For production via LLM API (GPT-4, Claude) — often the best cost/quality/speed trade-off.

Embeddings: Vector Representations of Text

Embeddings are the foundation of semantic search, deduplication, clustering, RAG. Quality critically affects downstream tasks.

Models. E5-large-v2, BGE-M3, multilingual-e5-large — strong multilingual embedders. sentence-transformers/paraphrase-multilingual-mpnet-base-v2 — fast option. For Russian: ru-en-RoSBERTa (Skoltech) performs well on semantic textual similarity.

Embedding quality evaluation uses the MTEB benchmark as standard. But top results on MTEB don't guarantee success on a domain dataset — we build domain-specific eval.

Fine-tuning embeddings. If standard models don't give the required Recall@k — contrastive learning on domain pairs with MultipleNegativesRankingLoss. How to perform this for domain data:

1. Collect 500–2,000 semantically similar pairs from your domain.
2. Apply MultipleNegativesRankingLoss with a batch size of 32–64.
3. Train for 1–3 epochs using AdamW (lr=2e-5).
4. Evaluate Recall@k on a held-out domain test set.

This approach yields a 5–15% improvement in Recall@k in practice.

Dimensionality and storage. E5-large: 1024 dim, float32 — 4KB per vector. For 10M documents — 40GB. Quantization int8 reduces to 10GB. FAISS IVF_PQ — more compact but with losses. Included in our deployment recommendations.

Information Extraction

Structured extraction is a frequent task. Examples: key contract terms, technical characteristics, dates and amounts from invoices.

1. Regex + rule-based. For INN, OGRN, amounts, dates — more reliable than neural networks. No data required.
2. NER + post-processing. For variable formats.
3. LLM with structured output. GPT‑4 / Claude with JSON schema — for complex documents. Cost: minimal per document. For 10k+ documents/day — we calculate the economics.

We guarantee a hybrid: regex/NER for typical fields + LLM for edge cases. Our guarantee is backed by years of production experience and more than 30 projects.

Work Stages

Prototype on existing data — 1-3 weeks. Production system with CI/CD — 1.5–2.5 months. Cost is calculated individually — get a consultation for a project estimate.

What's Included

• Model and pipeline architecture documentation
• Access to the model via REST API (FastAPI + ONNX)
• Client team training (2-hour webinar + Q&A)
• Accuracy guarantee on the agreed test set
• Months of post-delivery support (bug fixes, adaptation to new data)

Our Experience

Years of NLP projects from classification to RAG systems. The team includes ML engineers experienced with Hugging Face, spaCy, LangChain, MLOps. We use vLLM, Kubeflow, Weights & Biases — a production stack, not toys. Contact us to evaluate your NLP project within two days — request a free consultation on your text processing pipeline.